Shu Wang

Evolved Macro-Diversity: CDMA2000 and UMTS

2012-02-02T11:53:00.000-08:00

Macro-diversity typically means a special communication mode between a single mobile station and multiple base stations in a cellular network. It has been in CDMA standards as CDMA2000 soft handoff and WCDMA soft handover since the beginning, where it is a kind of spatial diversity in nature. Recently it is employed for cdma2000 BCMCS and UMTS MBS. The basic idea is to coordinate multiple base stations to deliver the same data stream to a mobile receiver in the down links and receive the signals from a mobile station from multiple base stations. Macro-diversity is possible for CDMA soft handoff because there is no hybrid automatic repeat request (HARQ) for voice data and no fast retransmission is necessary due to the strict delay requirement of voice service. The benefits of doing soft handoff on voice service include reduced transmission power and seamless mobility. On the other hand, it also challenges the mobile station's capability to handle additional multipaths.

In the scenario of high-rate data delivery, HARQ is necessary for taking advantage of inaccurate channel estimation as well as channel fluctuation. Since the channels between the mobile and each base station in its active list are generally different and there is no fast link among involved base stations, therefore it is inherently difficult to do soft handoff for high-rate data delivery service. In addition, the amount of fading resulted from soft-combining multiple channel may diminish and this may result less achievable time diversity gain. Therefore, macro-diversity is only applicable for the data delivery services, where there is no HARQ, for example, BCMCS and UMTS MBS. For the case of the macro-diversity of soft over the air combining, the achievable ergodic capacity is

C₁ = B * E{ log₂[ 1 + ( S₁ + S₂) / N ] }

However, things changed a little bit more recently. the simultaneous communication between multiple base stations and a single mobile is proposed for EV-DO Rev. C in a new term, single-carrier multi-link. For LTE-Advanced (LTE Release 10/11) , it is called Coordinated Multi-Point transmission and reception (CoMP). When two data streams from two base stations are independent from each, it essentially is a way of spatial multiplexing, in which interference cancellation is one of the key receiver element for the mobile station to achievable maximum throughput. However, considering the independent fast multiuser scheduling and HARQ are used by each access network or eNodeB, it is very challenging for mobiles to do successive interference cancellation. For the case of the spatial multiplexing without interference cancellation, the achievable ergodic capacity is

C₂ = B * E{ log₂[ 1 + S₁ / ( N + S₂ ) ] } + B * E{ log₂[ 1 + S₂ / ( N + S₁ ) ] } ≤ C₁

Hack Patriot Box Office for Watching Chinese Videos and TVs

2012-01-31T10:22:00.000-08:00

As requested by friends, this blog details a very simple way for watching Chinese TVs and videos for free. Compared with doing the same things on a computer, the suggested approach here is much more operation friendly and eco-friendly. I guess the total power consumption of this kind of MIPS-based media players should be no more than 15 watts. Meanwhile, the typical power consumption of a Intel or AMD PC or laptop CPU itself is between 50 watts and 100 watts. Even an Intel Atom CPU itself has a power consumption between 5 watts and 20 watts, as I can recall. Nowadays the power supply to a typical home desktop is not less than 250 watts. A powerful game desktop or work station easily demands a power supply of 400 watts or more.

Patriot Box Office High-Definition Media Player PCMPBO25 is a Realtek RTD1073DD SoC based networked media player made by Patriot Memory . It has about 128MB SPI flash, 128MB DDR2 SDRAM and a 400MHz MIPS core. One nice thing about this player is Patriot Memory hosts a very OPEN and friendly support forum for their media players and shares a lot of details of the firmware. This not only makes this player hard to be bricked but also enables many mods and hacks. One simple mod I am going to introduce here is to update its firmware for watching Chinese videos and TVs.

The procedure for updating its firmware can be found on Patriot Memory support forum. After slight modifications, it is copied here for your convenience.

Watch over composite hookup if possible. ( Comment: Though I have hacked many PBOs with watching over HDMI without any issues so far, YMMV.)
Download firmware.
Unzip/unrar
Copy "install.img" file to the ROOT directory of an FAT32 FORMATTED USB drive ONLY.
Put the USB drive into the front usb port of Patriot Box Office
Power up both PBO and TV and choose the PBO input on TV menu.
Go to SETUP -> SYSTEM -> SYSTEM UPDATE, & select SYSTEM UPGRADE
The screen will black out for about a few seconds until the update process initiates. The whole update process may reboot & resume, please DO NOT remove the usb drive UNTIL the screen goes back to the setup page.
(Optional) Additional update might be necessary if the remote doesn't work after the above update.

As far as I know, the newest unofficial firmware supporting China videos, movies and TVs is the one posted on HDP Fans Forum [11/2011].

What Is The Next for Mobile System Design? I: A Single-Cell Model Perspective

2012-01-20T10:50:00.000-08:00

Mobile system design usually starts from our understanding of wireless channels and the services customers are demanding. The properties of various wireless channels can help us understand the system design limitation we are facing and the potentials we may achieve. For example, COST 231 model, which was developed by European COST Action 231. Its variations are the most popular radio propagation models used in almost every wireless standardization body, including 3GPP, 3GPP2 and IEEE. Its modifications include COST 231-Hata Model and COST 231-Walfisch-Ikegami Model. One nice thing of COST 231 channel model is it helps us understand the tradeoff between reception and coverage we are facing in a typical single-cell environment.

Figure 1. Spectral Efficiency and Coverage Tradeoff

As shown in Figure 1, with a 300-meter-tall transmitter antenna, we can see that the path-loss changes 0.66 dB at every 90% coverage change, 1.39 dB at every 80% coverage change, 2.22 dB at every 70% coverage change and 3.18 dB at every 60% coverage change. In general, if you want more coverage, then you may lose some capacity especially on the cell-edge. Otherwise, you have to shrink your coverage.

Figure 2. What we want to achieve.

As shown in Figure 2, though there is a fundamental tradeoff between coverage and performance we are constantly facing in our system design, customers always desire their mobile network having both better coverage and higher performance for less. Now the challenge to us is how to push up the system design boundary. There are at least three major approaches available to push the envelope. They are 1) interference cancellation or management, 2) multi-antenna technology and 3) cells cooperation and relay.

Figure 3. Mobile System Design Options

Interference cancellation (IC) and management are the key ingredients for mobile network to achieve optimal performance. There are many ways to do interference cancellation, linear ICs (decorrelating detector, MMSE IC) and Nolinear ICs (joint detection, decision feedback IC ). Interference management can be done in time, frequency and space domain. OFDMA-liked multiplexing scheme is friendly to interference management. MIMO can help meet the demand of high data rate and high link quality. It can not only help improve link quality through spatial diversity and beamforming but also help achieve higher data throughput using spatial multiplexing and multiuser MIMO. The third weapon is heterogeneous transmission and deployment, which can help improve the network throughput as well as cell-edge user experience. Cooperation between cells is not something very new. Starting from 2G/IS-95, there has been soft handoff for macro-diversity. Additionally in 3G, we did it for broadcast multicase service over mobile networks, e.g., CDMA2000 BCMCS or UMTS MBS. However, all these cell cooperations are coordinated by MSC. More recently, LTE-Advanced standardized X2 interface between eNodeBs belonging to the same MME. This makes neighboring cells cooperation, such as corrdinated multi-point transmission and reception (CoMP), inter-cell interference coordination (ICIC) and relay, a reality. Similarly in CDMA2000 EV-DO Rev. C, there is a feature called single-carrier multi-link (SCML), which essentially extends the capability of multi-carrier devices in a single-carrier environment.

Work or Study Item List for LTE Release 11: LTE 2 Advanced ?

2011-12-16T15:08:00.000-08:00

From Chair's notes, there are more than 40 work or study items proposed for enhancing LTE Advanced or both LTE-Advanced and HSPA. They will be completed by September 2012.

Further Enhanced Non CA-based ICIC for LTE
LTE Carrier Aggregation Enhancements
Study on Coordinated Multi-Point Operation for LTE
Study on Enhanced Uplink Transmission for LTE
Study on further Downlink MIMO enhancements for LTE-Advanced
Study on Further Enhancements to LTE TDD for DL-UL Interference Management and Traffic Adaptation
Coordinated Multi-Point Operation for LTE
Provision of low-cost MTC UEs based on LTE
Proposed SI on LTE Coverage Enhancements
Improvements to LTE Relay Backhaul
Study on LTE Device to Device Discovery and Communication - Radio Aspects
Network-Based Positioning Support for LTE
Service continuity and location information for MBMS for LTE
LTE RAN Enhancements for Diverse Data Applications
Study on signaling and procedure for interference avoidance for in-device coexistence
Study on HetNet mobility enhancements for LTE
Study on RAN improvements for Machine-Type Communications
RAN overload control for Machine-Type Communications
Study Item on Further RAN Improvements for Machine-type Communications
study item proposal for LTE and HSDPA Carrier Aggregation
Enhancement of Minimization of Drive Tests for E-UTRAN and UTRAN
Signalling and procedure for interference avoidance for in-device coexistence
Study Item Proposal for Opportunistic Carrier Aggregation across 3GPP-LTE and WLAN
Carrier based HetNet ICIC for LTE
Study on further enhancements for HNB and HeNB
LIPA Mobility and SIPTO at the Local Network RAN Completion
Further Self Optimizing Networks (SON) Enhancements
SI: Mobile Relay for E-UTRA
Network Energy Saving for E-UTRAN
UE Over the Air (Antenna) conformance testing methodology- Laptop Mounted Equipment Free Space test
UE demodulation performance requirements under multiple-cell scenario for 1.28Mcps TDD
Uplink Transmit Diversity for HSPA – Open Loop
Non-contiguous 4C-HSDPA operation
Study on Measurement of Radiated Performance for MIMO and multi-antenna reception for HSPA and LTE terminals
Study on Inclusion of RF Pattern Matching Technologies as a positioning method in the E-UTRAN
Relays for LTE (part 2)
Enhanced performance requirement for LTE UE
Electromagnetic Compatibility (EMC) Requirements for Multi-Standard Mobile Terminals and Ancillary Equipment
SI: Passive InterModulation (PIM) handling for Base Stations
E-UTRA medium range and MSR medium range/local area BS class requirements
SI: Study of RF and EMC Requirements for Active Antenna Array System (AAS) Base Station
RF Requirements for Multi-band and Multi-standard Radio (MB-MSR) Base Station

There are additional 11 work or study items for further enhancing HSPA itself.

Eight carrier HSDPA
Uplink Transmit Diversity for HSPA – Closed Loop
Study on Uplink MIMO
Study on HSDPA multipoint transmission
Study item on HSPA enhancement for LCR TDD
Four Branch MIMO transmission for HSDPA
Uplink MIMO with 64QAM for HSUPA
Further Enhancements to CELL_FACH
HSDPA Multiflow Data Transmission
Single Radio Voice Call Continuity from UTRAN/GERAN to E-UTRAN/HSPA
SID: Introduction of Hand phantoms for UE OTA antenna testing

Evolved Handovers: EV-DO and LTE

2011-12-10T11:11:00.000-08:00

One key feature of any mobile communication system is to provide a mobility mechanism for mobiles to do fast and seamless switching between serving cells. There are many different handover mechanisms for achieving this goal. They include soft handover, which is mostly used for voice services, and hard handover, which is designed for data services. Hard handovers can be further classified as network-controlled handovers and mobile-based handovers. The interesting thing is if you look at the basic handover or handoff procedures I will explain in the next, you may feel a long-time debate on which entity , base stations or mobiles, should control handover or handoff.

Traditionally there is no standardized direct connection between two BTS's, even they both belongs to the same BSC. Therefore, there may be a long outage for a mobile do hard handover. For example, the default forward traffic channel MAC handover scheme of CDMA2000 EV-DO Rev. 0 usually results in 100~200ms outage. In order to minimize this outage for some delay-sensitive services, EV-DO Rev. A specially provides a new uplink channel, DSC (Data Source Control) channel, for mobile to indicate early knowledge of its upcoming handover. When a mobile starts a handover in EV-DO Rev. A network, it sends a forward cell switch indication, a new DSC, to a target BTS for a duration of DSCLength. Meanwhile, it is still receiving data from its current serving BTS until this mobile performs a DRCCover change to the target BTS. DRCCover is used by the mobile to specify its best serving BTS. During this moment, the queue transfer will be completed from the source BTS to the target BTS.

In LTE Release 8, things changed a little bit. A X2 connection between base stations, eNodeBs, is standardized and it is assumed to always exist as long as they belong to the same pooling area defined by a MME. The basic procedure for a LTE mobile to do handover becomes that the mobile sends Measurement Reports back to its current serving eNodeB and its serving eNodeB decides if to perform a handover and selects a target eNodeB. Source eNodeB then issues a Handover Request message to target eNodeB and passes necessary information. After target eNodeB accepts the request, it will prepare for it and acknowledge it after the preparation is done. As soon as source eNodeB receives Handover Request Acknowledge message, both a handover command and data forwarding are transmitted.

However, story doesn't stop here. Since a LTE mobile starts its handover when the radio link it sees from its serving eNodeB isn't very good, there is a possibility that it detects a radio link failure and can't successfully decode the handover command. In this case, it starts a RLF (Radio Link Failure) timer. Upon the expiration of RLF timer, the mobile searches for a good target eNodeB and tries to re-establish its connection with the target eNodeB while remain in connected state. During the period before a successful connection to eNobeB, source eNodeB will notice the changes and communicate with target eNodeBs through X2 interface to ensure the L1/L2 handover. If the re-connection failed, the mobile will switch from connected state to idle state and do a NAS recovery after that.

Wait a minute ... there is more. Usually RLF timer is optimized to be several hundred ms. This means the delay of RLF timer based handover can be relatively long. If the mobile can send re-connection request to target eNodeB before RLF timer expires and target eNodeB can request handover from source eNodeB instead of waiting, the handover delay can be reduced accordingly. In this case, the handover sounds more like to be mobile-initiated or mobile-based.

USB Flash Drive Recovery or Repair Options

2011-11-29T11:02:00.000-08:00

Someday your favorite and important USB flash drive will suddenly don't do its job. When you try to access your files in it, it may take hours to complete anything. When you try to format it, a pop-up message window will tell you "device media write protected" or "unable to complete format". Before you throw it into trash can, here are something you may try.

Windows, Right Click, Format, Both normal/quick format.
HDD LLF Low Level Format Tool
HP USB Disk Storage Format Tool
Active@KillDisk
Someone said it may work after put it in a freezer for a while. Who knows.
If it is a flash card, you may try to format it using a digital camera or mobile phone.

Anyway, if you succeed in repairing your USB flash drive, it may be a good idea for you not to use it for storing any critical files in the future.

Evolved Carrier Aggregation: CDMA2000 and LTE

2011-11-10T15:31:00.000-08:00

One advanced feature of 3GPP LTE-Advanced is carrier aggregation. It is an essential mechanism for LTE Release 10/11 to meet the peak-data rate, 1Gbps, requirement of IMT-Advanced of ITU-R. Beside this, it also helps LTE maintain backward compatibility, support symmetric/asymmetric operation modes and contiguous/non-contiguous aggregations. All these features help enable a variety of network deployment scenarios, such as hot-spot operations and flexible duplex.

Historically carrier aggregation is not something brand-new in standards. On 3GPP2 side, the IS-2000 standard supports a multi-carrier operation, Spreading Rate 3 or 3x. Spreading 3 is used when higher data rates are desired with more bandwidth available. Spreading 3 of multi-carrier operation can make IS-2000 not only is backward compatible with its IS-95 predecessor but also satisfies the requirements set forth by IMT-2000/3G at that time. With adding additional guard band, IS-2000 can be harmonization-ready with UMTS. Since IS-2000 essentially is circuit-switch based mobile system and is optimized for delay sensitive services, especially voice service. It assumes symmetric traffic between its uplink and downlink. Therefore its multi-carrier extension is also symmetric and very straightforward. It can be implemented either with a single RF carrier of direct spread of 3.6864 Mcps or with three separated RF carriers of 1.2288 Mcps each. Naturally this maintains backward compatibility and can even reuse existing channel cards and many hardwares.

In 2006, 3GPP2 upgraded its CDMA2000 high rate pack data (HRPD) standard, also known as EV-DO, from Rev. A to Rev. B. One key feature of Rev. B is the multi-carrier operation supporting a bandwidth from 1.25MHz up to 20MHz bandwidth and accordingly resulting a much higher peak data rate, 4.9xMbps downlink. This makes EV-DO not only directly competing but also compatible with UMTS. Different to IS-2000 1x RTT, EV-DO is a packet-switch based mobile system optimized for data services. It doesn't assume the balance between uplink and downlink. Though it can be symmetrically extended in the similar fashion as IS-2000 does and maintain nice backward compatibility, it is not optimal.

Similar to HSPA, EV-DO Rev. B supports three multi-carrier operation modes, symmetric mode, asymmetric modes and enhanced asymmetric mode. For the symmetric mode, additional mechanism, Multi-Link RLP, is introduced to help solve the MAC queue hole issue. This feature is software upgradable. In order to achieve higher efficient and flexibility, asymmetric operation mode and its enhancement are introduced with merging control overheads of difference carriers. ( Note: EV-DO Rev. B = Multi-Carrier EV-DO Rev. A + more )

For LTE-Advanced, story can go much further. The symmetric operation mode essentially is similar to EV-DO Rev. B and IS-2000 1XRTT except the extension of OFDMA signaling is much naturally scalable. The interesting part is on asymmetric operation mode. LTE-Advanced proposed a concept of carrier segmentation, which can also be found in the enhanced asymmetric mode of EV-DO Rev. B. LTE carrier segment is a pool of adjacent component carriers with a total bandwidth up to 110 resource block or 20MHz while it is up to 4 forward link carriers in EV-DO Rev. B.

And ... story will never stop here. One of the reasons is that carrier aggregation is naturally related to interference management and heterogeneous networks. I am sure that carrier aggregation with dynamic component carrier activation/de-activation and X2 interface between eNodeBs will enable a lot of innovative applications in the future.

Install Optware on Seagate Home NAS

2011-09-05T09:29:00.000-07:00

The Seagate GoFlex Home product line is a serial of network attached storage (NAS) devices made by Seagate for home computer network storage and backup. As reported, it essentially is a plug computer powered by a 1.2GHz Marvell Kirkwood ARM processor 88F6281 (ARMv5te based) teamed up with 128MB of Nanya RAM and 256MB of flash memory. My Seagate GoFlex Home NAS 2TB comes with a 2TB storage space, a gigbit ethernet port and an USB 2.0 port. Inside the storage enclosure is a 3.5-inch hard disk reported to be a Seagate Barracuda ST32000542AS. The ethernet port is reported to be driven by a Marvell 88E116R LAN controller.

Figure 1. Seagate GoFlex Home Network Storage System

Before installing Optware packages on a Seagate Plug, you need decide whether you should install them on the onboard flash memory or an external storage, such as the attached NAS or an attached USB thumb drive. In my case, I chose to straightly install the packages on the NAS and mount the installation subdirectory under the root "/". Whichever way you will choose, here are some considerations.

Try to conserve the limited resource on the onboard flash memory.
The external USB thumb drive should be ext2 or ext3 formatted.

The key package ipkg-opt to be installed can be download from NSLU2 repository. You can either wget it directly onto your Seagate Plug or download separatedly and upload to your installation drive.
Here are the lines for achieving this.

mkdir /opt

cd /home/YourUserName/GoFlex\ Home\ Personal/

mkdir opt

mount --bind /home/YourUserName/GoFlex\ Home\ Personal/ /opt

mount

The package is packed with gz compression so the next step is to unpacked it.

tar xvzf ipkg-opt_0.99.163-10_arm.ipk

tar xvzf data.tar.gz

ls /opt/etc/ipkg.conf

If everything went well, you will end up with debian-binary, control.tar.gz and data.tar.gz from the first unpacking. Additional subdirectories, such as bin, etc, share, and lib, from the second unpacking. After the installation, it is time to do some additional configuration and tests.

echo 'src seagateplug-stable http://ipkg.nslu2-linux.org/feeds/optware/cs08q1armel/cross/stable' >> ipkg.conf

echo 'src seagateplug-unstable http://ipkg.nslu2-linux.org/feeds/optware/cs08q1armel/cross/unstable' >> ipkg.conf

/opt/bin/ipkg update

Finally to make sure the package you installed works properly, you need make the following configurations.

ls /etc/init.d/rcS

cp rcS rcS.old

vi /etc/init.d/rcS

insert either "mount --bind /home/YourUserName/GoFlex\ Home\ Personal/ /opt" right before the line that says hostname
exit the editor.

vi /etc/profile

on the first line, type in "#!/bin/bash".
on the second line, type in "export PATH=/bin:/sbin:/usr/bin:/usr/sbin:/opt/bin:/opt/sbin".
exit the editor.

vi /root/.bash_profile

on the first line, type in "#!/bin/bash".
on the second line, type in "export PATH=/bin:/sbin:/usr/bin:/usr/sbin:/opt/bin:/opt/sbin".
on the third line, type in "mount --bin /home/YourUserName/GoFlex\ Home\ Personal/ /opt"
exit the editor.

ipkg update

IVR codes for Linksys/Cisco SPAxxxx or PAP2

2011-01-05T14:40:00.000-08:00

A list for who knows what it is.

Check DHCP 100#
Enable/Disable DHCP 101#
Check IP address 110#
Set IP address 111# (use * for period)
Check Network Mask 120#
Set Mask 121#
Check Gateway 130#
Set Gateway 131#
Check MAC 140#
Check Firmware version 150#
Check DNS 160#
Set DNS 161#
Check Web Server Port 170#
Enable/Disable Web Server 7932# (may require password)
Manual Reboot 732668#
User Factory Reset (only changes user settings) 877778#
Factory Reset (all non default settings change) 79738#

How to Broadcast Multimedia Contents? VII Network Layer or Steam Layer Design

2010-07-15T10:45:00.000-07:00

[How to Broadcast Multimedia Contents? I Introduction]
[How to Broadcast Multimedia Contents? II Lessons from The Channel]
[How to Broadcast Multimedia Contents? IV Hierarchical Modulation]
[How to Broadcast Multimedia Contents? V Overloaded Transmission and IC]
[How to Broadcast Multimedia Contents? VI Open-Loop MIMO for Broadcast Multicast Services]

Many broadcast/multicast infrastructures are engineered for delivering a wide range of contents, such as streaming media, multicast media and even IP datacast. Though all of them have the similar capabilities of delivering a pretty-much same set of services, their bear technologies and network layer or stream layer designs are varied. In terms of physical layer, ATSC uses 8 VSB while DVB, FLO and ISDB use OFDM in their air interface designs. In terms of stream layer design, DVB-H uses IP, ISDB uses MPEG TS, T-DMB and FLO use their own mapping between application layer and MAC layer logic channels.

Fading Broadcast Channel Capacities: III Scalar Fading Channels

2010-06-15T09:02:00.000-07:00

In wireless broadcast multicast services (BMS), a standard assumption is that each receiver knows something about the channel h, usually referred as channel side information (CSI) or channel quality information (CQI). This is a pretty reasonable assumption when the channel is fading slowly inside the design boundary since there are pilot symbols available for the receiver to estimate CQI. Since the channel and transmitted signals are independent to each other, the ergodic capacity of the fading channel with receiver side information is given by

C_fading( SNR, h ) = E log( 1 + |h|² SNR ) ≤ C_AWGN( E(|h|²) SNR ).

This means fading hurts or reduces the capacity in general if the transmitter knows nothing of the fading. This is different to the case that assumes the transmitter can estimate channel through CQI feedback and therefore can do some precoding on broadcast signals.

Since a statistic analysis on a log(*) probability function is non-trivial, one approach is to apply the well-know Maclaurin expansion on C_fading( SNR, h ) and obtain a polynomial series of it

C_fading( SNR, h ) = -ln^-12 Σ_n=1[ (-SNR)ⁿE(|h|ⁿ)/n ].

From here, it is much easy for us to find some interesting results in the following.

Low SNR Region

C_fading( SNR, h ) ≈ ln^-12 SNR E(|h|) - ln^-12 SNR² E(|h|)² K( h^1/2)/2

where K() denotes Kurtosis function.
High SNR Region

C_fading( SNR, h ) ≈ E log( |h|² SNR )

It is hard to evaluate the above in general since the random valuable h is inside the nonlinear function log( ). However, a simple close-form solution may be possible for some special cases, including Rayleigh channel model, Weibull channel model, and Nakagami-m channel model

Quantify Channel Fading

There are many literatures discussing how to quantify the amount of fading a channel may have. The parameter, Kurtosis, is one of them. Kurtosis essentially is the normalized fourth moment of a realvalued random valuable and indicates the ”peakedness” of a probability distribution. A high kurtosis means a large variance due to infrequent extreme deviations, which results in a sharp ”peak” and fat ”tails”. Another similar measurement is the amount of fading of a real-valued random valuable, which indicates the severity of channel fading in communications. The Kurtosis and amount of fading of major fading distributions are compiled in Table 1. A similar compilation can be found in [Shamai 01].

Table 1. The Kurtosis and amount of fading of various fading channel models

Fading Broadcast Channel Capacities II: Gaussian Broadcast Channel

2010-04-24T12:59:00.000-07:00

Instead of the traditional single-coverage model, a layered broadcast model with a two-layer coverage is considered here. In this model, the broadcast station (BS) broadcast two layers of signal to all mobile stations (MS) in the covered area. The signal for the inner coverage has the achievable rate of R₁ and the achievable rate for the outer coverage is R₂, where R₁ > R₂. The MS's located near to the outer coverage edge may only be able to reliably decode the data stream of a low rate R₂ while the MS's close to the BS can decode both data streams with a high sum rate R₁. There many ways for achieving this two-layer broadcasting, including frequency-division multiplexing (FDM), time-division multiplexing (TDM) and superposition precoding (SPC).

Figure 1. Achievable capacity region of broadcast channel. The coverage difference is 6dB.

One key aspect of studying two-layer broadcast is the finding of the achievable broadcast channel capacity, which states that a little throughput sacrifice on the users near to the coverage edge may lead to a big increase for the users with good reception [Cover 72]. This concept is illustrated in Figure 1, where the achievable rates of FDM, TDM and SPC are compared. It shows that SPC can outperform FDM and TDM most of the time. With moving up the network operation point, e.g., the single-coverage operation point (r₂, r₂), a little bit along the SPC curve a higher throughput r₂+ \Delta > r₂ is achievable. There are many methods implementing SPC. The most popular one is hierarchical modulation. More generally, SPC can be implemented by overloaded CDM.

Fading Broadcast Channel Capacities I: Introduction

2010-04-14T12:44:00.000-07:00

Broadcast multicast service (BMS) has increasingly been popular for delivering multimedia content to mobile users. BMS can be implemented through either a dedicated digital broadcast infrastructure like DVB-T/H/S2, MediaFLO and DMB or a 3rd generation and beyond radio access network like UMTS or cdma2000 network. Traditional digital broadcast air interface and network are designed with the tradeoff between the achievable capacity and intended coverage in mind. The actual throughput is limited by the maximum transmit power and the worst channel condition so that each user in the coverage area can reliably receive services. Therefore, all covered users share services with same quality. The users under good reception condition may not have advantages, even though their achievable throughput can be much higher. In addition, there are also rising interests in upgrading existing digital broadcast systems with more services for new users while be able to keep existing users unchanged, delivering additional or better QoS's to users with advanced receivers while still be able to guarantee other users' services, and providing unequal protection on digital contents with high spectral efficiency [ DVB Project 00, FLO Forum 07, Jiang 05, 3GPP2 07]. This is also encouraged by the recent advance in scalable video coding with an extension of the H.264/MPEG-4 AVC video compression standard, which provides possible adaptation capabilities for the BMS applications with no feedback necessary. Many technologies are under investigation for these goals, e.g., rateless coding, superpositioning precoding (SPC), multiple-input multiple-output (MIMO) and selective retransmission. Backward compatibility and implementation complexity are among the major concerns in upgrading existing systems. Among the candidates, SPC, e.g., hierarchical modulation, is one of the promising technologies for upgrading existing systems with larger coverage and more QoS options while maintaining strictly backward compatibility.

In system design, BMS is traditionally taken as a special case of the well-known broadcast channel model, in which one transmitter serves multiple receivers and the transmitter either know or has no prior information of the channels between itself and each receiver. For BMS, usually it is assumed that the transmitter has no such prior chanel information. So far, it is well-known that maximum sum rate of Gaussian broadcast channel is achievable with SPC, with which two independent signals are superimposed at the transmit side and successive interference cancellation is employed at the receive side. SPC has been widely proven and included in various standards, such as DVB-T, FLO, UMB, etc., and is under study for DVB-H. Currently, SPC is implemented with hierarchical modulation. In UMB and FLO, e.g., two bit streams, named enhancement layer and base layer, are coded, bit-mapped and modulated a QPSK/QPSK symbol stream, which is transmitted over orthogonal-frequency division multiplexing (OFDM). In this case, the channel fading is thought to limit the achievable spectral efficiency of the broadcast channel and complicate the receiver design. Therefore, a good channel coding is usually employed for compensating the impairments by the channel fading.

The recent progress in multiuser receiver design and CDMA has brought new attentions on how to broadcast layered contents over broadcast channels [Verdu 99, Shamai 01, Wang 08]. From an information-theoretic perspective, the channel fading on CDMA signals isn't so bad as we thought [Shamai 01]. The achievable capacity of a CDMA multiuser fading channel may be higher than a single-user channel corrupted by the same fading distribution. In fact, it asymptotically approaches the single-user Gaussian channel capacity if the system load, which is defined as the number of symbols per chip, is high enough and a optimum receiver is used. On the other hand from a signal processing standpoint, the performance of successive interference cancellation (SIC) is asymptotically close to optimum receivers while the power imbalance between the desired signal and interference is large enough [Verdu 98]. Bringing these two observations together, a precoded OFDM was proposed to broadcast layer-coded content through fading channels [Wang 08] with superimposing an additional layer of Multi-Carrier Code-Division Multiplexing (MC-CDM) signal on top of the existing OFDM transmission. The power setting of the two layers of signals are specially controlled so that a SIC receiver can be employed at the receive side to separate the signals. Here, the proposed precoded OFDM is generalized as an overloaded Quasi-Orthogonal CDM (QO-CDM) and the impact of fading on broadcast channel with QO-CDM is analyzed.

How to Broadcast Multimedia Contents? VI Open-Loop MIMO for Broadcast Multicast Services

2010-02-13T22:18:00.000-08:00

One most well-known space-time block coding (STBC) design is Alamouti code, which is the simplest open-loop orthogonal STBC. Alamouti code was designed for a two-transmit antenna system. It is a rate-1 code. It is the first open-loop encoding method with full diversity. Though orthogonal STBC has the advantages of relatively easy receiver design and full diversity, it is known that full-rate STBC don’t exist for more than 2 transmit antenna. From previous discussion, if two orthogobal STBCs are superimposed together and each of them experiences different channel fading, there would be multi-layer diversity in addition to potential superposition precoding gain. This means one STBC signal layer is in a bad channel condition, the other STBC signal layer may not. Therefore, the transmission of more than one layers may have higher achievable spectral efficiency.

Figure 1. A Quasi-Orthogonal Space Time Block Coding Example

One widely discussed example of quasi-orthogonal STBC is shown in Figure 1. From a receiver perspective, this quasi-orthogonal STBC obviously has a larger signal constellation size. In general, larger the signal constellation size is, high the spectral efficiency is achievable. Besides this, it may have so-called multilayer diversity when it experiences channel fading since each orthogonal STBC signal experiences different fading. However, there is no free lunch. The demodulation complexity may increase exponentially with the number of superimposed STBC layers.

How to Broadcast Multimedia Contents? V Overloaded Transmission and Interference Cancellation

2010-02-01T12:41:00.000-08:00

Though hierarchical modulations have been widely adopted for enhancing broadcast multicast services, several issues are still left for future enhancements. The first consideration is the inter-layer interference (ILI) between layers. The ILI from enhancement layer(s) to base layer(s) is not additive white Gaussian. The base-layer achievable spectral efficiency is actually dented by ILI more than expected. In addition, for example, when orthogonal frequency division multiplexing (OFDM) is employed on the carrier, there is a frequency selectivity issue on the layered transmission in fading channels, especially when the channel bandwidth is far more than its coherent bandwidth. With the combination of hierarchical modulation and OFDM, the base layer signal and the enhancement layer signal experience the same channel fading. There is no multi-layer diversity, which can help boost the achievable throughput.

Figure 1. An example of overloaded OFDM transmission for upgrading existing OFDM multicast/broadcast

One simple overloaded transmission solution to upgrade existing OFDM based broadcast multicast traffic channel is shown in Figure 1. With this scheme, legacy mobiles can seamlessly operate in the upgraded network without additional change. The control overhead signal part is same. The pilot part is reused. Only the traffic channel part is upgraded. The new traffic channel part is layer-modulated and transmitted with an additional pre-coded OFDM modulated enhancement layer, where the symbols are precoded with Walsh-Hadmard matrix before OFDM. In an additive white Gaussian channel, this scheme has the superposition precoding (SPC) gain since it essentially is an implementation of SPC. However, the interference from the enhancement layer is randomized due to additional Walsh-Hadmard spreading. In the fading channel, additional multi-layer diversity gain is achievable, since the base layer and the enhancement layer are operating in different signal spaces. A general overloaded OFDM transmission structure is shown in Figure 2.

Figure 2. A general overloaded OFDM transmission structure

Figure 3. An example of strictly backward compatible upgrade of existing OFDM based broadcast multicast network

Another advantage of this upgrade architecture is there is little interference between legacy air interface (AI) and new AI. The base-layer signals of the neighboring legacy local operating infrastructure (LOI) and new LOI still are soft-combinable. The enhance-layer signals of the neighboring legacy LOI and new LOI are not overlapped to each other due to its limited overage. Legacy mobiles can properly decode the base-layer of both legacy LOI and new LOI with no problem. The only degradation is that legacy mobiles may not be able to decode the enhancement-layer of the new LOI.

From an information theoretic perspective, the schemes shown in Figure 1 and 2 are interesting. However, the questions remain, can it be implemented in realities or are the overloaded signals be reasonably easy to demodulate and decode? The answer is yes and an interference cancellation receiver should be employed. A successive interference cancellation (SIC) receiver can be enough to do the trick. On the other hand, SIC is one of the advanced receivers which are widely discussed and implemented in commercial products in realities. The reason can be explained in the following and in Figure 4.

Figure 4. Asymptotic multiuser efficiency (AME) of various multiuser receivers

From a receiver design standing point, the effective energy of user 1 e₁ is always upper bounded by the actual energy A²₁. This typically is quantified through a parameter called multiuser efficiency or asymptotic multiuser efficiency (AME). Multiuser efficiency or ratio between the effective energy and actual energy, e₁/A²₁, of an user, depends on the signature waveforms, received signal-to-noise ratio (SNR) and the employed detector and is always not larger than 1. The AME of several popular advanced receivers are plotted in Figure 4. In Figure 4, it shows for a low signaling loading factor β = K/N, the linear multiuser receivers like decorrelator and MMSE can achieve near-optimum spectral efficiency. However, for a high loading factor β, nonlinear multiuser receivers can help obtain the optimal spectral efficiency. The loading factor β is a parameter what percentage of the system degree of freedom are used by the transmitter. For a CDMA system, it usually denotes the ratio between the number of active users and the spreading gain. However, additionally when the received signal power imbalance between desired signal and interference is large, the performance of SIC is asymptotically close to optimum receivers. For a two-user case, the imbalance requirement is A₂/A₁ > (1- ρ²)/|ρ|. Fortunately this imbalance requirement also is the prerequisite to SPC transmission.

From the above discussion, it is easy for us to find that the combination of overloaded transmission and successive interference cancellation can be right ingredients to achieve fading Gaussian broadcast channel capacity.

What An Engineer Needs to Know About Patent Laws? I: Background

2010-01-22T14:46:00.000-08:00

Intellectual Property Values

On August 9, 2000, CNN Money reported, a U.S. appeals court set a sooner-than-expected end to Eli Lilly and Co.'s reign as the sole marketer of Prozac, the popular antidepressant drug, a development that sent the pharmaceutical company's stock down by more than 30%. [“Eli Lilly gets Prozac blues “, CNN ] It is about $36 billion in Lilly stock value, roughly a third of the pharmaceutical giant’s then market capitalization. On June 7, 2002, New York Times reported that royalties from inventions earn an estimated $150 billion/year worldwide and are expected to grow 30% annually over the next 5 years. [“Trying to Cash in on Patents”, New York Times] From 1993 to 2003, IBM alone was reported to earn well over $10 Billion in revenue from licensing out its patents and be awarded over 22,000 patents, more than the 10 largest U.S. IT companies combined, including Intel, Microsoft, Sun, Dell and Apple. Meanwhile, Microsoft was reported to spend about $8 billion on R&D and filed about 3,000 patent applications each year in US. Obviously intellectual property is becoming a key asset of modern corporations. From a recent British survey, 40% of a company's value is not shown in any way on its balance sheet. For some big corporations, including Walt Disney, Microsoft and P&G, more than 80% of their market value is said to be in intellectual property assets.

U.S. Patents granted, 1790–2008. Source: wikipedia.com and uspto.gov

Patent Rights

In US Constitutional Authority, Article 1, Section 8, Clause 8, it states, “The Congress shall have Power ... to promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries”. The former US Presidents Thomas Jefferson said that “The issue of patents for new discoveries has given a spring to invention beyond my conception.” and Abraham Lincoln said, “The patent system added the fuel of interest to the fire of invention.” World Intellectual Property Organization states, “Under such regional systems, an applicant requests protection for the invention in one or more countries, and each country decides as to whether to offer patent protection within its borders.”

US Government grants a right to exclude, which means a US patent provides its owner with the legal right to prevent unauthorized making, using, selling, offering for sale in US and the importation into US, of the invention set forth and claimed in the patent. In exchange, the inventor must disclose how to make and use the invention. However, it is Not a Right to Practice. It does not grant patent owners the right to practice the invention. ( e.g., government regulation may interfere ) From a business perspective, with the right to exclude, the granted patent rights may permit setting prices at a level not possible without patent protection and help erect barriers for entry into a market. They can also bring in more revenue through licensing or assignment. On the other hand, if defensively used as part of a patent portfolio, they can be used to trade ( cross license ) rights to exclude, maintain product differentiation, develop reputation as innovator and help with credibility as well as advertising.

How to Broadcast Multimedia Contents? IV Hierarchical Modulation

2009-08-28T09:17:00.000-07:00

[How to Broadcast Multimedia Contents? I Introduction]
[How to Broadcast Multimedia Contents? II Lessons from The Channel]
[How to Broadcast Multimedia Contents? V Overloaded Tx and IC]
[How to Broadcast Multimedia Contents? VI Open-Loop MIMO for BCMCS]
[How to Broadcast Multimedia Contents? VII Network Layer or Steam Layer Design]
[Contribution to 3GPP2 Next Generation Technologies Ad Hoc Group (NTAH) 2007]
[On Enhancing Hierarchical Modulations, 2008 IEEE Int. Sym. on BMSB ]

As shown in Figure 1, hierarchical modulation, also called layered modulation, is one of the techniques for multiplexing and modulating multiple data streams into one single symbol stream, where the base-layer symbols and enhancement-layer symbols are synchronously overlapped together before being transmitted. When hierarchical modulation is employed, users with good reception and advanced receiver can demodulate more than one layer of data streams. For a user with conventional receiver or poor reception, it may be able to demodulate the data streams embedded in low layer(s), e.g, the base layer only. From an information-theoretical perspective, hierarchical modulation is taken as one of the practical implementations of superposition precoding, which can help achieve the maximum sum rate of Gaussian broadcast channel with employing interference cancellation by receivers. From a network operation perspective, a network operator can seamlessly target users with different services or QoS’s with this technique. However, traditional hierarchical modulation suffers from inter-layer interference (ILI) so that the achievable rates by low-layer data streams, e.g. the base-layer data stream, can be dented by the interference from high-layer signal(s). For example, for the hierarchically modulated two-layer symbols with a 16QAM base layer and a QPSK enhancement layer, the base layer throughput loss can be up to about 1.5bits/symbol with the total receive signal-to-noise ratio (SNR) of about 23 dB. This means, due to ILI, there is about 1.5/4 = 37.5% loss of the base-layer achievable throughput with 23dB SNR. And the demodulation error rate of either the base-layer and enhancement-layer symbols increases too. From a practical implementation point-view, it is also known that the severe amplitude and phase fluctuations of wireless channels can significantly degrade the receiver demodulation performance since the demodulator must scale the received signal so that the result signals is within the dynamic range of the followed analog-to-digital convertor (ADC) or, more generally, the receiver processing region, mostly with automatic gain control (AGC). Even though pilots may be available for assisting the receiver channel estimation and equalization, there are channel estimation errors, especially when the channel coherent time is short. If the channel is estimated in errors, it can lead to improperly compensated signals and incorrect demodulation even in the absence of noise. On the other hand, multicarrier transmission, e.g. orthogonal frequency-division multiplexing (OFDM), is widely used for broadcast multicast services (BCMCS) as well as next generation wireless systems, due to its high diversity gain and high spectral efficiency with simple receiver design. However, the advantages of OFDM, specially when it is modulated by high-order signal constellations, are counter-balanced by the high peak-to-average-power ratio (PAPR) issue. High PAPR of modulated signals can significantly reduce the average output power of the high-power amplifier (HPA) at the transmitter due to more back-offs. It also increases the receiver demodulation and decoding errors and therefore limits the throughput of whole transceiver chain. Therefore it is important to understand and optimize regular hierarchical modulations for the best achievable performance.

Figure 1. Enhanced hierarchical modulation example: QPSK/QPSK

In this contribution, the regular hierarchical modulation is firstly extended by allowing additional rotation on the enhancement layer signal constellation. The generalized hierarchical modulations are then studied and analyzed from four different perspectives, such as achievable capacity [Figure 2], modulation efficiency [Figure 4], demodulation robustness and peak-to-average-power ration (PAPR) when it is combined with the popular OFDM. At first, the achievable capacities of hierarchical modulations over Gaussian broadcast channel are studied from an information-theoretical perspective. As an example, the capacity of a regular 16QAM is tore down into the equivalent capacities of a base layer and enhancement layer. It is shown that there is a capacity loss on the base layer due to the inter-layer interference (ILI) from the enhancement layer [Figure 2]. And this capacity loss can be mitigated by properly rotating the enhancement signal constellation. From a signal-processing perspective, it is known that the capacity loss is also related to the Euclidean distance profile of the hierarchical modulation signal constellation. For example, in high signal-to-noise ration (SNR) region, the symbol error rate usually is dominated by the minimum Euclidean distance. Obviously, with properly rotating the enhancement layer signal constellation and maximizing the minimum Euclidean distance, the resulted symbol error rate will decrease. Additionally, for tracking Euclidean distance profile changes, several parameters like effective signal power, effective SNR and modulation efficiency are discussed too. After this, hierarchical modulations are analyzed from an implementation perspective with considering channel estimation errors, which includes both channel amplitude estimation errors and channel phase estimation errors. It is shown that the demodulation robustness of hierarchical modulations can also be controlled by changing the Euclidean distance profile. Finally hierarchical modulations are discussed from a transmit power efficiency perspective when it is combined with multicarrier transmission. With avoiding high back-offs and maximizing average output power, it shows that high RF transmitter power efficiency is achievable by properly rotating the enhancement layer signals. With the analyses from different aspects of hierarchical modulation, a in-depth understanding of it can be achieved.

Figure 2. Capacity tear-down of 16QAM, a hierarchical modulation perspective

Figure 3. Bit error rates of hierarchical modulation and inter-layer interference

Figure 4. Modulation efficiencies of hierarchical modulations

How to Broadcast Multimedia Contents? III Scalable Video Coding

2009-08-12T14:22:00.000-07:00

It is very challenging to deliver multimedia contents through wireless links. Diverse receivers may request the same video with different bandwidths, spatial resolutions, frame rates, computational capabilities. Heterogeneous networks with unknown network conditions. Wired and wireless links, time-varying bandwidths. One Example is when you originally code the video you don’t know which client or network situation will exist in the future. Probably have multiple different situations, each requiring a different compressed bit stream. It needs a different compressed video matched to each situation. Possible solutions include 1) compress and store MANY different versions of the same video, 2) real-time transcoding (e.g. decode/re-encode), and 3) scalable video coding.

The procedure of scalable coding includes decomposing video into multiple layers of prioritized importance, coding layers into base and enhancement bit streams, and progressively combining one or more bit streams to produce different levels of video quality. Examples of scalable coding with base and two enhancement layers, such as 1) Base layer, 2) Base + Enh1 layers, 3) Base + Enh1 + Enh2 layers. Three basic types of scalability (refine video quality along three different dimensions).
Temporal scalability → Temporal resolution
Spatial scalability → Spatial resolution
SNR (quality) scalability → Amplitude resolution
Multiple types of scalability can be combined to provide scalability along multiple dimensions

How Wide A Wideband Channel Should Be?

2009-07-11T13:58:00.000-07:00

[Frequency Selectivity of A 1.2288MHz, 3GPP2, TSG-C, Working Group 3, C30-20090511-028]

How wide should a channel be before it is called wideband? Dictionary.com says it is "responding to or operating at a wide band of frequencies". I guess this is pretty much the one in most people's minds. Wikipedia.org gives us a more technical definition,"a system is typically described as wideband if the message bandwidth significantly exceeds the channel's coherence bandwidth". Basically it says if a channel can be called wideband largely depends on the coherence bandwidth it has. The question then becomes what coherence bandwidth is and how wide a typical coherence bandwidth can be. For example, should a CDMA2000 channel, which has a bandwidth of 1.22288MHz, be called wideband or not? Why can a 5.0MHz WCDMA channel be called wideband? Let's find out here.

Coherence Bandwidth

Coherence bandwidth is a statistical parameter indicating how fast a channel changes in frequency and the frequency range over which the channel can be considered "flat". Narrower a channel's coherence bandwidth is, more frequency selectivity it has and more frequency diversity gain the communication system can achieve. In general, the channel's "flatness" depends on both the cell size and operating environment. The factors includes, macro-cell or micro-cell, urban or suburban, indoor or outdoor, line-of-sight or no-line-of-sight detection, and detection threshold in a typical receiver design. Fundamentally, the coherence bandwidth of a multi-path channel is an inverse to its delay spread. And the delay spread of a channel can be quantified through the measuring of root mean squared (RMS) delay spread or maximum excess delay spread. Maximum excess delay spread can be taken as an upper bound reference.

As we know, RMS delay spread is known to follow a log-normal distribution, which is similar to that of log-normal shadowing (LNS). In fact, RMS delay spread is correlated to log normal shadowing and its median grows as some power of distance. RMS delay spread has been modeled and simply quantified in the form:

Δ _rms = E^1/2{ (d - d₀)² } ≈ Δ₀ · d^ε · y

where d is the distance in km, ε is an exponent between 0.5 and 1.0, and y is a log-normal variant. The correlation coefficient value for suburban and urban data was shown to be about -0.75, which indicates that for a strong signal ( positive LNS ), the delay spread is reduced, and for a weak signal condition ( negative LNS ), the delay spread is increased. In [1], Sousa, et. al., reported the 90th percent rms delay spread to be 1.2 μs in suburban Toronto. In [2], Ling, et. al. observed that the 90th percent rms delay spread was 1.7 μs in Lakehurst NAES, New Jesey. In [3], Baum reported the 77th percent rms delay spread was 1 μs, the 94th percent rms delay spread was 2 μs in Rolling Meadows, Chicago.

Figure 1. The statistic model of delay spread

Now, considering the fundamental chip rate of 1.2288Mcps of CDMA2000 mobile communication standards, a 70%-90% RMS delay spread is between 1-2 chips, which is about a 3dB-coherence bandwidth of 25 – 60 subcarriers with the assumption of 180 subcarriers per 1.2288MHz. Therefore, a CDMA2000 1x channel statistically has 3 ~ 7 coherence bandwidths and it should be called narrowband instead. For a 5MHz WCDMA channel, 12 ~ 28 coherence bandwidths should be observed. It can be called wideband.

Impact of Cyclic Delay Diversity (CCD)

The impact of multiple Tx antennas on channel delay spread also depends on the employed multi-antenna techniques. CCD is one of the most open-loop multi-antenna techniques. When CCD is employed by the transmitter, the total delay spread will increase. This usually results in more fluctuations in the frequency domain of channel response.

Example: Coherence Bandwidth of OFDM Channels

Figure 2. OFDM Coherence Bandwith

[1] E. Sousa, V. Jovanovic, C. Daigneault, “Delay spread measurements for the digital cellular channel in Toronto”, IEEE Trans. on Vehicular Technology, Nov 1994
[2] J. Ling, D. Chizhik, D. Samardzija, R. Valenzuela, “Wideband and MIMO measurements in wooded and open areas”, Lucent Bell Laboratories,
[3] K. Baum, “Frequency-Domain-Oriented Approaches for MBWA: Overview and Field Experiments”, Motorola Labs, IEEE C802.20-03/19, March 2003
[4] L. Greenstein, V. Erceg, Y. S. Yeh, M. V. Clark, “A New Path-Gain/Delay-Spread Propagation Model for Digital Cellular Channels,” IEEE Transactions on Vehicular Technology, VOL. 46, NO.2, May 1997, pp.477-485.
[5] A. Algans, K. I. Pedersen, P. Mogensen, “Experimental Analysis of the Joint Statistical Properties of Azimuth Spread, Delay Spread, and Shadow Fading,” IEEE Journal on Selected Areas in Communications, Vol. 20, No. 3, April 2002, pp. 523-531.
[6] Spatial Channel Model AHG (Combined ad-hoc from 3GPP & 3GPP2), “Spatial Channel Model Text Description ”, 3GPP, 2003
[7] H. Arslan and T. Yucek, Estimation of Frequency Selectivity for OFDM based New Generation Wireless Communication System, WWC 2004.

How Much Feedback Is Enough for MIMO? VI Rank Deficiency

2009-03-20T10:27:00.000-07:00

[How Much Feedback Is Enough for MIMO? I Introduction]
[How Much Feedback Is Enough for MIMO? II Channel Estimation]
[How Much Feedback Is Enough for MIMO? III Codebook Design]
[How Much Feedback Is Enough for MIMO? IV Channel Quantization]
[How Much Feedback Is Enough for MIMO? V Feedback Reliabilities]
[3GPP2 TSG-C WG3 C30-20090511-030]
[3GPP2 TSG-C WG3 C30-20090511-032]

The adoption of multi-antenna techniques is believed to be able to provide additional antenna gain, diversity gain, multiplexing gain and interference cancellation gain. They can help improve link quality and increase link throughput. Multi-antenna techniques are believed to be critical in meeting the demand of high data rate and high link quality and can be employed for both forward link and reverse link transmission. However, there are many issues which should be carefully considered when multi-antenna techniques are implemented. These issues include the rank deficiency of actual MIMO channels, the limitations of mobile terminal's RF design and the impact of multi-antenna techniques on other services in bandwidth-limited situations.

In theory, the achievable capacity of a MIMO channel grows linearly with the minimum of transmit and receive antenna sizes. In reality, the achievable spatial multiplexing gain depends on both channel scattering of underlying and antenna configurations of both sides instead of the geometric limitation, min{ N_tx, N_rx }. The scattering statistics of a MIMO channel is usually quantified with angular intervals. The antenna array configuration is characterized by the area or size limitation in the unit of wavelength λ and the shape. Without considering AT size, the achievable spatial multiplexing gain is limited by spatial scattering. For example, in the case of a typical 4x4 MIMO mobile communication scenario and without the limitation of access terminal's size, it is observed that less than 1% of the users are able to use rank 4 and around 90% users have either rank 1 or 2. However, it is non-trivial to “squeeze” multiple antennas and RF circuits into a mobile phone in actual commercial mobile terminal design, especially when you need additional planning on the power consumption, mechanical limitation, antenna spacing and supported frequency bands of the mobile terminal. Currently, there are many radio interfaces already enabled in most mobile phones, such as GPS, bluetooth, WiFi, etc. However, from an RF design perspective, there is an antenna spacing requirement that the separation between antenna elements should be larger than 0.5λ in order to maximize spatial diversity gain. This can be translated into about 7.5 cm or 3.0 inches for a 2GHz operating band, as an example. Therefore, there usually is a tradeoff between the physical size and achievable performance in each mobile terminal design. For an AT with the physical size of a few times of wavelength, e.g., about 0.5~3λ, the achievable spatial multiplexing gain is limited by the angle spread, AT size and C/I ratio. This means for practical multi-antenna mobile devices, the expected spatial multiplexing gain mostly is less than 3.

Expected Spatial Degree of Freedom. 6 spatial cluser, angle spread = 35^o, dual-polarized antenna array, f = 2GHz

How Much Feedback Is Enough for MIMO? V Feedback Reliabilities

2009-03-08T09:06:00.000-07:00

Figure 1. A Noisy Feedback Channel Model

The reverselink channel model is a concatenation of a Gaussian channel and binary erasure channel, which are independent to each other. In generally, the reliability of reverselink is controlled by both channel fading and received SNR. When the erasure rate ε_r is high, it means the amount of fading of reverselink is very high. Higher erasure rate also means it takes the forwardlink transmitter longer time to accurately filter out a proper forwardlink precoding word and it usually yields higher MIMO precoding mismatch given a certain channel coherent time. Since the unreliable symbols are erased based on their received SNR, the left symbols are more reliable and their reliability is mostly decided by γ_RL. In this case, the well-known sphere-packing upper bound of Gaussian channel reliability function is

Figure 2. The rate-reliability region with γ_RL = 7dB

How Much Feedback Is Enough for MIMO? IV Channel Quantization

2009-02-25T10:26:00.000-08:00

[How Much Feedback Is Enough for MIMO? I Introduction]
[How Much Feedback Is Enough for MIMO? II Channel Estimation]
[How Much Feedback Is Enough for MIMO? III Codebook Design]
[How Much Feedback Is Enough for MIMO? V Feedback Reliabilities]
[How Much Feedback Is Enough for MIMO? VI Rank Deficiency]

Generally, MIMO channel quantizer or CQI generation, maps the input channel estimation vector to the index of a codeword in the codebook. The decode will do the reverse. It is similar to the vector coding in EVRC, AMR, MPEG-4, etc. The designing a best codebook as well as finding the general boundary of Voronoi cell is NP-hard.

Figure 1. MIMO Precoding Mismatching

With Figure 1, it is shown that there are multiple issues involving MIMO precoding mismatching. In most existing MIMO beamforming systems, the receiver tracks the channel norm information for link adaptation purpose and the phase information for beamforming precoding. In this case, D_h can be rewritten by

D_h = 2M σ_h² [ 1 - ( 1- D_θ )^1/2 ]

with D_θ denoting the phase quantization deviation. A lower bound for distortion rate of channel quantization for MIMO beamforming is

R( D_θ ) ≥ max{ - M log₂[ 2 - 2( 1- D_θ )^1/2 + σ_h²/σ_h² ], 0 }

for each beam. With the feedback rate of R, it also tells us that the minimum precoding mismatch for forwardlink MIMO beamforming is

D_θ ≤ 1-[1-2^-R/M-1 + σ_h²/(2σ_h²)]²

Figure 2. The rate-distortion region with M = 4

How Much Feedback Is Enough for MIMO? III Codebook Design

2009-02-17T20:24:00.000-08:00

[How Much Feedback Is Enough for MIMO? I Introduction]
[How Much Feedback Is Enough for MIMO? II Channel Estimation]
[How Much Feedback Is Enough for MIMO? IV Channel Quantization]
[How Much Feedback Is Enough for MIMO? V Feedback Reliabilities]
[How Much Feedback Is Enough for MIMO? VI Rank Deficiency]

Figure 2. Voronoi cell and various bounds

MIMO beamforming mismatch upper bound depends on the codebook design. The maximum MIMO beamforming mismatch can be determined by the largest radius of the codebook’s Voronoi cell { V_i : 1 ≤ i ≤ 2^R }, which in general is the solution to the disk-covering problem that still is open. Instead of finding the exact boundary for the Voronoi cell V_i, a heuristic approach using sphere-packing bound and sphere cap to approximate the actual polytope boundary can be used. The result is an approximate of the sphere packing solution, in which all spheres are supposed to be non-overlappedly placed. With this approach, sphere caps are overlapped with each other in space but the interior of them has the same area as the Voronoi cell. The border of this sphere cap is named sphere-packing boundary. The relationship between sphere-packing boundary and Voronoi cell is shown in Fig. 2. For an uniform random codebook of size 2R in M-dimensional Euclid space, the area of a Voronoi cell is given by

A( V_k) = 2π^M / [ 2^R Γ(M) ]

where Γ(*)denotes the gamma function. A heuristic upper bound of MIMO beamforming mismatch is given by

D_θ ≥ { (M-1)/M [ 1 - ( 1 - 2^-R )^1/(M-1) ] }^1/2

How Much Feedback Is Enough for MIMO? II Channel Estimation

2009-01-24T14:38:00.000-08:00

[How Much Feedback Is Enough for MIMO? I Introduction]
[How Much Feedback Is Enough for MIMO? III Codebook Design]
[How Much Feedback Is Enough for MIMO? IV Channel Quantization]
[How Much Feedback Is Enough for MIMO? V Feedback Reliabilities]
[How Much Feedback Is Enough for MIMO? VI Rank Deficiency]

MIMO beamforming with finite-rate feedback is modelled as a noisy Gaussian binary erasure feedback channel depicted. In reality, the receivers estimate channels with the pilots sent by transmitter. Accuracy of the channel estimation depends on both forwardlink design and receiver design. The pilot transmission is important for receiver to efficiently estimate CSI. antenna. An overview of pilot-assisted transmission (PAT) including pilot placement and channel estimation can be found in [Tong 04]. There are two popular pilot patterns, time multiplexed pilots (TMP) and superimposed pilots (SIP), receiving much attention for MIMO CSI estimation. They are shown in Fig. 1. Optimal pilot placement was investigated in [Dong 02]. TMP is a typical example of orthogonal pilot design where pilot symbols and data symbols are separated in time and/or frequency domain, which makes them orthogonal to each other. With orthogonal pilots, the CSI estimation and data demodulation can be done separately which may lead to simple receiver design [Dong 02]. SIP does the opposite. In SIP design, pilots and data nonorthogonally share the same time period and frequency band. In this case, joint channel estimation/demodulation and the demodulation with pilot interference cancellation are among the most popular receiver design techniques [Coldrey 06]. After channel estimation, the receiver chooses a beamforming vector from a shared MIMO precoding codebook. This is called channel quantization. It means the receiver actually feeds back the chosen precoding index(es) to transmitter(s) instead of channel response for MIMO precoding.

Figure 1. Pilot patterns for channel estimation

In general, the channel quantization distortion is decided by channel quality, channel estimation and codebook design. Given σ_h², the channel estimation mean squared error (MSE), the minimum rate at the channel quantization mean squared distortion D_h is given by

R( D_h ) ≥ M log₂[ σ_h² / ( D_h/M + σ_h² ) ]

Meanwhile, the lower bound to the unbiased MSE σ_h² is given by Cramer-Rao lower bound (CRLB), which is defined as the inverse of the Fisher Information Matrix (FIM).

How Much Feedback Is Enough for MIMO? I Introduction

2009-01-05T09:52:00.000-08:00

[How Much Feedback Is Enough for MIMO? II Channel Estimation]
[How Much Feedback Is Enough for MIMO? III Codebook Design]
[How Much Feedback Is Enough for MIMO? IV Channel Quantization]
[How Much Feedback Is Enough for MIMO? V Feedback Reliabilities]
[How Much Feedback Is Enough for MIMO? VI Rank Deficiency]

Figure 1. MIMO model with feedback

Multi-antenna systems have received much attention over the last decades, due to their promise of higher spectrum efficiency with no transmit power increase. Combining multiantenna transceiver with relay network is essential not only to provide comprehensive coverage but also to help relieve co-channel interference in existing wireless systems in a cost effective fashion. For multiple-input multiple-output (MIMO) transmission, it is well-known that their performance and complexity can be improved by making channel state information (CSI) available at the transmitter side. This is usually achieved through a reverselink CSI feedback channel from receiver, e.g., there is a reverslink channel quality indicator channel (RCQICH) for CSI feedback in UMB (Ultra Mobile Broadband), a 3.5G mobile network standard developed by 3GPP2. In practice, CSI received by transmitters is not perfect and suffers from various impairments and limitations that include round-trip delay, channel estimation error, codebook limitation, etc. Therefore the actual link throughput is degraded. This kind of degradation becomes more serious if the end-to-end capacity is considered for a multi-hop MIMO relay network.

Figure 2. Noisy Gaussian binary erasure feedback channel with channel

MIMO beamforming with quantized feedback has been intensively investigated since 1990s [1]. MIMO channel quantization as well as codebook design in general is a NP-hard Voronoi decomposition problem. The Voronoi region for a uniform random codebook is known to be upper-bounded by the disk-covering problem solution and lower-bounded by the sphere-packing problem solution. These two problems themselves are still open. MISO/MIMO beamforming systems with perfect CQI Lloyd vector quantization (VQ) [2], different channel model [3] or different performance metrics [4], [5] have intensively been investigated. It is linked to Grassmannian line packing problem [6]. However, most of existing work is done without considering pilot design, channel estimation and the reliability of feedback, even though they are among the most important components of actual multi-antenna systems. In reality, MIMO CSI is estimated with forwardlink common pilot channels sent from each transmitter antenna. An overview of pilot-assisted transmission (PAT) including pilot placement and channel estimation can be found in [7]. In most multiantenna systems, pilot channels are designed to be orthogonal to other channels and periodically sent by transmitter. Nonorthogonal pilot design like superimposed pilots (SIP) has recently received much attention for channel estimation too [8]. Optimal pilot placement was investigated in [9]. Besides pilot design, the feedback capacity and reliability have intensively been investigated over decades too. Though feedback doesn’t increase the capacity of memoryless channels [10], [11], a feedback coding scheme with the decoding error probability decreasing more rapidly than the exponential of any order is achievable [12]. Since CSI feedback plays such a critical role in MIMO transmission, it is desired to understand how MIMO pilot and codebook design affects system behavior, what are the tradeoffs, etc. And these problems become more critical when a multi-hop MIMO relay network.

The feedback and sharing of CSI and/or network status information (NSI) help wireless relay network achieve high throughput and reliability with a little overhead increase. For example, CSI feedback helps nodes realize distributed cooperation for increasing the throughput and reliability of wireless relay networks. Cooperation diversity for wireless relay network has heavily been investigated in the past several years. The concept of distributed cooperation diversity is knowingly pioneered by Sendonaris et al. [13], where the transmitters cooperate with each other by repeating symbols of others. It shows that a higher rate is achievable with this cooperation. Almost at the same time, this concept is also developed through other techniques such as code combining [14], coherent soft combining [15], power control [16] and later opportunistic routing [17]. Most of them are implemented with CSI feedback in the assumption. Besides this, from a network perspective, it is known that NSI feedback can also assist each source terminal or relay terminal to shape the dynamic behavior of the network and increase network agility through proper resource allocation [18]. Due to the limitation of the measuring, link capacity and network resource in reality, however, most CSI or NSI sent back by receivers is neither perfect nor sufficient in nature. It is interesting and important to understand the effect of imperfect feedback on wireless link and network, which are still not clear from many perspectives.

Reference

[1] D. Gerlach and A. Paulraj. Spectrum reuse using transmitting antenna arrays with feedback. In Proc. Int. Conf. Acoust., Speech, Signal Processing, pages 97–100, Adelaide, Australia, April 1994.

[2] A. Narula, et al. Efficient use of side information in multiple-antenna data transmission over fading channels. IEEE J. Select Areas in Communications, 16(8):1423–1436, October 1998.

[3] K. K. Mukkavilli, A. Sabharwal, E. Erkip and B. Aazhang. On beamforming with finite rate feedback in multiple-antenna systems. IEEE Trans Info. Theo., 49:2562–2579, October 2003.

[4] P. Xia and G. B. Giannakis. Design and analysis of transmit beamforming based on limited-rate feedback. In Proc. IEEE VTC, September 2004.

[5] J. C. Roh and B. D. Rao. Performance analysis of multiple antenna systems with vq-based feedback. In Proc. Asilomar Conference 2004, Pacific Grove, CA, November 2004.

[6] D. J. Love, R. W. Heath and T. Strohmer. Quantized maximal ratio transmission for multiple-input multiple-output wireless systems. In Proc. Asilomar Conf., Pacific Grove, CA, Nov. 2002.

[7] L. Tong,B. M. Sadler and M. Dong. Pilot-assisted wireless transmissions: general model, design criteria, and signal processing. IEEE Signal Processing Mag., 21(56):12–25, November 2004.

[8] M. Coldrey and P. Bohlin. Training-based mimo syetems: Part i/ii. Technical Report (http://db.s2.chalmers.se/), (R032/033), June 2006.

[9] M. Dong and L. Tong. Optimal design and placement of pilot symbols for channel estimation. IEEE Trans. on Signal Processing, 50(12):3055–3069, December 2002.

[10] C. E. Shannon. The zero error capacity of a noisy channel. IRE Trans. Inf. Theory, 2(3):8–19, September 1956.

[11] Y. H. Kim. Feedback capacity of the first-order moving average gaussian channel. IEEE Trans. on Inf. Theory, 52(7):3063–3079, July 2006. [12] A. J. Kramer. Improving communication reliability by use of an

intermittent feedback channel. IEEE Trans. Inf. Theory, 15:52–60, January 1969.

[13] A. Sendonaris, E. Erkip and B. Aazhang. User cooperation diversity - part i/ii. IEEE Trans. Commun., 51(11):1927–1948, Nov. 2003.

[14] T. E. Hunter and A. Nosratinia. Cooperative diversity through coding. In Proc. IEEE int. Symp. Info. Theory, page 220, 2002.

[15] J. N. Laneman. Cooperative Diversity in Wireless Networks: Algorithm and Archiectures. Ph.D. Thesis, MIT, Cambridge, MA, 2002.

[16] N. Ahmed, M. A. Khojastepour and B. Aazhang. Outage minimization and optimal power control for the fading relay channel. In IEEE Information Theory Workshop 2004, pages 458–462, Oct. 2004.

[17] C. K. Lo, R. W. Heath and S. Vishwanath. Opportunistic relay selection with limited feedback. In Vech. Tech. Conf. 2007, Dublin, Ireland, Apr. 2007.

[18] Special issue on networks and control. In IEEE Control Systems Magazine, February 2001.

[19] H. Blcskei, R. U. Nabar, . Oyman and A. J. Paulraj. Capacity scaling laws in mimo relay networks. IEEE Trans. Wireless Communications, pages 1433–1444, June 2006.

[20] Bo Wang, Junshan Zhang and Anders Host-Madsen. On the capacity of mimo relay channels. IEEE Transactions on Information Theory.

Peak to Average Power Ratio II: An Introduction

2008-12-22T13:31:00.000-08:00

A Signal Processing Perspective

The statistic properties of the PAPR of multi-carrier signals can be described by CCDF (Complementary Cumulative Distribution Function). If we assume the frequency-domain symbol is complex Gaussian distributed. When the number of subcarriers, L, become large, the instantaneous power of each multi-carrier signal chip can be modeled by a Chi-distributed signal with two degree of freedom.

CCDF( γ ) = Pr( PAPR > γ ) = 1- Pr( PAPR <= γ ) = 1 – [ Pr( p <= γ ) ]^L >~ 1 – ( 1 – e^-γ )^L

Figure 1. A statistic modeling of PAPR

A Coding Perspective

Given a code of length n, coding rate R, what is the achievable region of triplets (R, d, PMEPR) ? What is the relationship between PMEPR and the minimum Euclid distance (mED) d* ? All these questions belong to a Sphere Packing Problem. Given a codeset of q-ary code c with length n, what is the relationship between the size of the codeset and minimum Hamming distance (mHD) D ? This is a Sphere Packing Problem again.

Figure 2. A geometric modeling of PAPR

An Implementation Perspective

Figure 3. Rapp’s SSPA model

Figure 4. AM/AM characteristics of the Rapp SSPA model, P = 2.

Popular PAPR Reduction Approaches

Clipping: In-band distortion mostly is negligible. But out-of-band distortion is more serious.
Filtering and Signal Processing

time-invariant linear filter results in less peak regrowth and lower PAPR than DFT filter in general, if there is no spectral masking.
Partial Transmit Signaling (PTS): divide/Group into clusters and each of them is done with a smaller IFFT. [Muller and Huber, 97]
Tone Reservation (TR): inserting anti-peak signals in unused or reserved subcarriers. The objective is to find the time-domain signal to be added into the original time-domain signal such that PAPR is reduced. [Tellado, 00]

Coding: The idea is to select a codeword with less PAPR. it still is an open problem to construct codes with both low PAPR and short Hamming distance.
Selected Mapping (SLM): it is based on selecting one of the transformed blocks for each data block, which has the lowest PAPR. [Bauml, Fisher and Huber, 96]
Constellation Optimization

Tone Injection (TI): the basic idea is to increase the constellation size so that each of the points in the original basic constellation can be mapped into several equivalent points in the expanded constellation.
Active constellation extension (ACE): similar to TI. [Krongold and Jones, 03]

Peak to Average Power Ratio I: OFDM PAPR Reduction

2008-12-08T09:03:00.000-08:00

[Contribution to 3GPP2 Next Generation Technologies Ad Hoc Group (NTAH) 2007]

With the upcoming deployment of wideband wireless network with throughput greater than 100Mbps over high frequency bands such as 5-GHz band and the adopting of multicarrier modulations, more and more challenges are brought to system and hardware design. OFDM, frequently referred as multi-carrier modulation, is becoming the de facto standard for next-generation wideband wireless networks. However, one of the critical issues of OFDM as well as other multicarrier modulation scheme is its high peak-to-average power ratio (PAPR), which usually requires large backoff and highly efficient high power amplifier (HPA), large dynamic range analog-to-digital converter (ADC), high linearity up-converter, etc. These requirements lead to expensive hardware systems that are difficult to design. Hence it becomes more and more important to alleviate the burden of hardware design with employing advanced PAPR reduction technologies.

In this tutorial contribution, OFDM RF design challenges and PAPR reduction technologies are presented. They are discussed in terms of both theory and implementation with many examples. Especially a patent search is given too. There are five major parts in this tutorial. In the first part, an introduction to OFDM, the RF design challenges and the PAPR issue is presented. We outline the challenges, which include high power peak, linearity limitation, image rejection, phase noise and distortion, etc., brought to each component of OFDM RF design. Here we focus on high peak power and try to solve the PAPR issue, which is defined from both signal processing and coding perspectives. The problem of PAPR issue is outlined from implementation perspective with the discussion of the effectiveness of signal clipping, which is known as one of the simplest PAPR reduction technique. In the second part, an overview of most popular PAPR reduction approaches is given. It includes coding, signal processing and filtering, selection mapping, signal constellation optimization, etc. The pros and cons of these approaches are compared in terms of performance and implementation complexity. In the third part, PAPR reduction techniques adopted in existing standards are presented and discussed. We cover some of the most important standards including GSM, WCDMA, LTE and UMB. In the fourth part, many PAPR reduction patent examples are presented, followed by a presentation of our recent contributions to PAPR reduction. Our approaches are simple and efficient, with low implementation complexity on the receiver. The conclusions and further works are outlined in the last part.

In summary, this tutorial is intended to provide a comprehensive overview of PAPR reduction form OFDM for a wide array of audiences. It includes not only the background theory, implementation considerations and related standards but also our recent contributions.

Interference Cancellation: IV A Blind Receiver Design Perspective

2008-11-22T09:20:00.000-08:00

[ Interference Cancellation. I. A Short Overview of Multiusr Detection ]
[ Interference Cancellation: II. A Conventional Receiver Design Perspective ]
[ Interference Cancellation: III. A Signal Subspace Perspective ]

While the conventional signal model provides a foundation for both optimal and conventional multiuser receiver design and the subspace signal model aids understanding of the underlying signal structure, neither is simple enough for developing blind multiuser receivers for high-speed CDMA systems [Andrews 05]. In order to address the near-far problem with minimum prior knowledge and computational complexity, a blind multiuser signal model and blind multiuser receiver design framework are presented here. Within this framework, the blind receiver only requires several previously received symbols in addition to its own signal signature(s), amplitude(s) and timing(s). Different to the conventional multiuser model and subspace signal model [Verdu 98, Wang 98], there is no signal signature or signal subspace basis of interfering signals necessary and no signal signature estimation or signal subspace separation procedure required in the proposed detection framework. Based on this model and detection framework, several optimal blind linear multiuser detectors are individually developed and analyzed with maximum likelihood (ML), MMSE and least squares (LS) criteria. In order to further reduce the complexity, some implementation considerations are outlined. In addition, I compared the proposed multiuser receivers with existing ones from several practical implementation prospects. For each of these blind linear multiuser receiver, I not only evaluate its link-level performance but also discuss how it behaves in a large-scale system. It shows that there is an additional noise enhancement in the proposed detection framework due to the limited number of previous knowledge but its computation complexity and detection delay still is lower than most existing multiuser receivers. In a large-scale system with large spreading gain and high SINR, the asymptotic performance of the proposed blind multiuser receivers are close to the conventional ones.

In general, one of the major difficulties in developing blind multiuser receivers with either the conventional signal model or subspace signal model is that the signal signatures {s_k : k ≠ 1} or the signal subspace matrix U_s are unknown beforehand. In most blind multiuser receivers, either the signal signature matrix S and the subspace transform matrix Ф are required to be estimated along the detection of desired signal, which is b₁ in this paper. Instead, I propose a known blind signature matrix S, which is constructed by simply concatenating available information known by user 1 into a L x M matrix, so that

S = [A₁s₁ r₁ r₂ ... r_M−1 ]= SA[ e₁ B] + N = SAB + N

where {r_m : m = 1, 2, ... , M−1} are (M − 1) previously received symbols, e_l is a K × 1 identity vector with a 1 as the lth element and 0’s as the rest, the K × 1 vectors b_m denotes the data sent by all K users with rm and the data matrix B is

B = [ b₁ b₂ ... b_M−1 ] = [g F^H ]^H

The proposed blind receiver design framework

A comparison between the blind receiver design framework and other detection approaches

A performance comparison of various multiuser receivers

Interference Cancellation: III A Signal Subspace Perspective

2008-11-20T10:02:00.000-08:00

In realities it is known to be difficult to directly and precisely estimate the signal signatures {s_k : k ≠ 1} for taking advantage of well-developed optimum or conventional multiuser detection schemes. In Figure 1, the design of a linear MMSE interference cancellation receiver for CDMA systems is shown as an example. As we can see, there are at least two challenges in the implementation. The first one is you need know the signal signatures of all involved users. The second one is it requires the computation-intensive matrix inverse operation. Design challenges like these make the conventional interference cancellation methodology unattractive in practical applications.

Figure 1. The challenges in employing conventional interference cancellation design. An example of linear MMSE interference cancellation

Now it is known that interference cancellation is able to be designed with a signal subspace model and statistic signal processing techniques for reconstructing the conventional detectors. Signal subspace methods are empirical linear approaches for dimensionality reduction and noise reduction in signal processing. They have attracted significant interest and investigation in the context of antenna array signal processing and speech signal processing for a long time. In later 1970s and early 1980s, G. Bienvenu and L. Kopp (1980) and R. O. Schmidt published their pioneer work applying signal subspace approaches on array signal processing. It is worth mentioning the well-known multiple signal classification (MUSIC) scheme introduced by R. O. Schmidt has been widely studied for estimating direction of arrivals (DOA) or frequency of arrivals (FOA). In 1901, Karl Pearson suggested the principal component analysis (PCA) approach, which essentially is similar to signal subspace approaches and widely applied in audio and speech signal processing. It is notable that Xiaodong Wang and Vincent Poor suggested further applying this concept on blind multiuser receiver design in 1998. The basic idea behind signal subspace approaches is to transform a series of samples, e.g., time-domain correlated samples, into a set of usually uncorrelated or less correlated representations in a linear subspace.

In the subspace signal model, the received signal vector r is modelled by a combination of the signal subspace bases {u_sk : 1 ≤ k ≤ K} according to

r = U_s φ + n

where U_s = [ u_s1 u_s2 . . . u_sK ], φ is a vector defined by

φ = Φ A b

With Φ being a K × K matrix. The original signal signature matrix S can now be expressed as

S = U_s Φ .

One most attractive feature of the subspace signal model is that the signal subspace bases {u_sk : 1 ≤ k ≤ K} are much easier to be blindly estimated than the actual signal signature waveform so that the blind receiver design can be simplified. In theory, these signal bases can be estimated by applying subspace decomposition on the autocorrelation matrix R

R = E{ rr^H } = [ U_s U_n ] diag{[Λ_s Λ_n]} [ U_s U_n ]^H

where U_n denotes the noise subspace bases.

Figure 2. Mathematical illustration of signal subspace linear MMSE interference cancellation

With the signal subspace approach, the linear MMSE interference cancellation shown in Figure 1 now can be redesigned in a different way. This is shown in Figure 2 and 3. The estimation and separation of signal and noise subspaces essentially help identify the signal signature of the desired components from the received signals. On the other conventional MMSE receiver can be blindly constructed with the signal and noise subspaces bases. No explicit signal signature estimation is necessary.

Figure 3. The receiver structure of signal subspace linear MMSE interference cancellation

Interference Cancellation: II A Conventional Receiver Design Perspective

2008-10-25T11:29:00.000-07:00

[ Interference Cancellation. I. A Short Overview of Multiuser Detection ]
[ Interference Cancellation: III. A Signal Subspace Perspective ]
[ Interference Cancellation: IV. A Blind Receiver Design Perspective ]

Introduction

Interference cancellation provides a promising alternative to the conventional or optimum detectors in multiuser detection. Interference cancellation methods typically require less implementation complexity while practically o ering similar performance. The idea behind interference cancellation is to estimate the multiple access and/or multipath induced interference and then to subtract the interference estimate from the received signal. Hence, compared to other multiuser detection schemes, interference cancellation pays more attention on the estimation of the multiple access interference (MAI). Different schemes for the MAI estimation lead to different interference cancellation schemes. Actually, interference cancellation detector will cancel the interfering signal exactly provided that the decision was correct and channel information is known. Otherwise it will double the contribution of the interferers. The main alternatives for implementation of interference cancellation are parallel hard interference cancellation (PIC) and serial/successive hard interference cancellation (SIC), while many other variants on these basic principles have also been developed. With conventional PIC, all user are simultaneously demodulated and detected in a parallel behave. With conventional SIC, a decision for the symbol of the stronger user is made rst, the interference from this user is subsequently removed in the the next stronger user's receiver before the next user's receiver make its decision and so on.

On this blog, we consider a synchronous DS/CDMA system and review the principles of interference cancellation. several soft interference cancellation schemes, including direct interference cancellation detector, MAME interference cancellation detector and MMSE interference detector, are introduced with di fferent MAI estimation schemes. I show that, besides MAME interefence cancellation, the proposed direct interference cancellation detector has the same performance of the classic decorrelating detector while the MMSE multiuser detection and the MMSE interference cancellation actually are the same detectors.

System Model

Figure 1. Multiuser Detection System Model

We consider a single-cell synchronous DS/CDMA model and assume that there are K active users in the cell, the data { b_k[n]: k = 1, 2, ..., K } of these K users are individually spread using the corresponding spreading sequences { c_k = [c_1k c_2k ... c_{L_ck}]^T : k = 1, 2, ..., K; } with the spreading gain L_c and synchronously received from these users through multipath channel and corrupted additive white Gaussian noise (AWGN) with the variance ¾2 n [3]. The channel is a multipath channel with up to P resolvable paths and corrupted by AWGN. The baseband representation of the received signal due to user k is given by

r_k(t) = Σ_p=1^Pα_pkA_k[n]b_k[n]c_k(t-nT-τ_p) + n_k(t)

where α_pk is the gain of the pth path of user k’s signal and b_k[n] is the nth bit sent by user k. We assume that the { b_k[n] : k = 1, 2, . . . , K } are independent and identically distributed random variables with E{b_k[i]} = 0 and E|b_k[i]|² = 1. The parameters {c_k(t) : k = 1, 2, . . . , K} denote the normalized spreading signal waveform of K users during the interval [0, T] and {A_k[n] : k = 1, 2, . . . , K} are the signal amplitudes at time t = nT. Without loss of generality, the P propagation delays from the base station to user k are ordered such that 0 ≤ τ₁ ≤ τ₂ ≤ . . . ≤ τ_P .

At the receiver side, the received signal passes through chip-matched filter (CMF) φ (t) and then RAKE combiner. The combined output r (t) is

r (t) = A₁b₁c₁(t − nT − τ₁) ⊗ φ (t − τ₁) + m_ISI (t) + m_MAI (t) + n (t)

Figure 2. A typical matrix representation of conventional multiuser detection system model

Interference Cancellation

Figure 3. The block structure of a basic interference cancellation detector.

As in Figure 3, it shows that there are usually two basic stages in interference cancellation realization. At the fi rst stage, the MAI from other users are reconstructed. At the second stage, the MAI is removed from the received signal and the nal decision is made from the rest signal through a matched lter. Thus, the key part in interference cancellation is how to estimate MAI as effciently as possible.

Figure 4. The structure of a direct interference cancellation, an equivalent to multiuser decorrelation detector

Figure 5. The structure of a linear MAME interference cancellation, an equivalent to multiuser MAME detector

Figure 7. The structure of a linear MMSE interference cancellation, an equivalent to multiuser MMSE detector

Interference Cancellation: I. A Short Overview Multiuser Detection

2008-10-08T08:56:00.000-07:00

[ Interference Cancellation: II. A Conventional Receiver Design Perspective ]
[ Interference Cancellation: III. A Signal Subspace Perspective ]
[ Interference Cancellation: IV. A Blind Receiver Design Perspective ]
[Toward Forward Link Interference Cancellation, CDMA Development Group (CDG) Technology Forum 2006]

CDMA cellular network capacity is known to be interference-limited since the same spectrum is shared by many users and there exists a near-far problem due to multiple access interference (MAI). Multiuser receiver is highly regarded as one of the promising interference management techniques improving spectrum efficiency and achieving high-data rates for wireless multimedia communication. It has been intensively investigated over the last two decades and received much attention for next-generation radio access network [Andrews 05, Wang 05]. Optimum multiuser receivers and conventional multiuser receivers are known to be able to solve the near-far problem at the knowledge of the signature information of all users [Verdu 98]. However this assumption isn’t always consistent with practical situations where the receiver may know only the signatures of the expected signals not interfering signals. Recent research work is mostly devoted to semiblind or blind multiuser receiver design and also signal signature estimation [Verdu 98, Madhow 98, Andrews 05]. It is known that most blind implementation of conventional multiuser receivers achieve the same performance as conventional multiuser receivers providing enough pilots or training signals available [Honig 95, Torlak 97, Wang 98, Zhang 02]. For example, with sufficiently training on receiver, blind minimum mean squared error (MMSE) receiver is known to achieve the performance of conventional MMSE detector [Verdu 98]. If enough signals are available for signal subspace separation or signal signature estimation, subspace-based blind multiuser receivers can achieve the performance of various conventional multiuser detectors too [Wang 98]. As long as interfering signal structure is pretty stable during the signal detection procedure, these blind multiuser receivers can work well with constant performance. They may not function well when the multiuser channel or system load experiences dynamic changes. However, this happens very frequently in reality, especially when the channel coherence time is not large enough. Therefore it is important to design multiuser receiver that requires a limited number of previous signals with better channel tracking capability and possible low implementation complexity. And it is also interesting to understand how the number of employed previous signals affect the performance of blind multiuser receivers and what are the tradeoffs between complexity and performance of blind multiuser receiver design in this case and in general too.

Figure 1. A List of Conventional Multiuser Detection Design Approaches

In the development of advanced multiuser receivers, it is known that a proper received signal model can give a lot of help with multiuser receiver design in addition to the understanding of received signals. There are two most popular multiuser signal models which have extensively been discussed and employed for receiver design and performance analysis. They are the conventional multiuser signal model and the subspace-based multiuser signal model [Verdu 98, Wang 98]. In the conventional signal model, each received symbol is expressed as a linear combination of actual signal signatures with their amplitude and timing [Verdu 98, Honig 95, Zhang 02]. The theory of optimal multiuser receivers and the conventional receivers are well developed with this model [Verdu 98]. Most related blind multiuser receivers are also developed either by explicitly estimating the signal signature [Torlak 97] or by removing interfering signal components using adaptive filtering techniques, e.g., blind receiver design with Wiener [Honig 95] and Kalman filter [Zhang 02] techniques. Though the conventional signal model provides a natural view of received signals, the involved signature waveforms information are unknown in reality and it requires considerable processing to obtain them before detection. To compensate for the weakness of the conventional signal model, the subspace signal model was developed with the statistic signal spectral analysis techniques termed subspace-based signal processing [Wang 98]. Subspace-based signal processing techniques are previously developed for array signal processing since 1970s [Schmidt 86, Hero 98]. In the multiuser subspace signal model, each received signal is taken as a linear combination of signal subspace bases, which are obtained by subspace analysis on the correlation matrices of the received signals. The subspace signal model can also be considered as the result of parametric signal modelling, which provides an in-depth description of the received signals. Though the subspace-based approach does not require explicit estimation of each user’s signature waveform and the adaptivity speed can be improved with some subspace tracking techniques [Yang 95], the signal subspace formation procedure itself is not trivial.

For a better design of multiuser receivers, the evaluation and understanding of multiuser receiver are traditionally based on the link-level parameters like asymptotic multiuser efficiency (AME) and near-far resistance (NFR) in addition to signal-to-interference/noise ratio (SINR) and bit-error rate (BER) [Verdu 98]. These parameters show the capability of a multiuser receiver to reject various interference. Among them, One of the most important measurements is SINR and most other parameters can be derived from it. For example, the AME of a multiuser receiver is the ratio between the actual multiuser receiver output SINR and the corresponding single-user output signal-to-noise (SNR) in high SNR region. The AME shows how steep the slope of a multiuser is when its BER goes to zero in logarithmic scale. The best achievable AME value is 1, which means the receiver works exactly the same way as if there is no interference at all. The worst value is close to 0 when the outputs of a multiuser receiver are completely determined by interference. The NFR of a multiuser receiver is defined by the minimum AME over the received energies of all other users. It shows the receiver’s ability to reject the worst-case interference. However, link level evaluation usually depends on the actual employed receiver structure and the signature waveforms. Recently several large-system measurements are developed to evaluate multiuser receiver’s behaviors in a system with large processing gain and lots of users [Tse 99]. In a large-scale system, one concept termed effective interference describes how the total received interference can actually be taken as a sum of other individual user’s effective interference. Effective interference itself is independent to the particular realization of random signal signatures. Extended from the concept of effective interference, effective bandwidth and user capacity are the terms describing how the system resource, in terms of power and degree of freedom, are assigned to users for achieving their target SINRs. I will show that these parameters can also be derived from asymptotic SINR too. One interesting thing here is these large-system evaluations only depend on the power distribution, system load, target SINR and receiver design, not the actual employed signal signatures for each user. This make it easy for giving some insights of multiuser receiver behaviors in an actual wireless communication environment.

While the conventional signal model provides a foundation for both optimal and conventional multiuser receiver design and the subspace signal model aids understanding of the underlying signal structure, neither is simple enough for developing blind multiuser receivers for high-speed CDMA systems [Andrews 05]. In order to address the near-far problem with minimum prior knowledge and computational complexity, a blind multiuser signal model and blind multiuser receiver design framework are firstly presented here. With this framework, the blind receiver only requires several previously received symbols in addition to its own signal signature(s), amplitude(s) and timing(s). Different to the conventional multiuser model and subspace signal model [Verdu 98, Wang 98], there is no signal signature or signal subspace basis of interfering signals necessary and no signal signature estimation or signal subspace separation procedure required in the proposed detection framework. Based on this model and detection framework, several optimal blind linear multiuser detectors are individually developed and analyzed with maximum likelihood (ML), MMSE and least squares (LS) criteria. In order to further reduce the complexity, some implementation considerations are outlined. In addition, I compared the proposed multiuser receivers with existing ones from several practical implementation prospects. For each of these blind linear multiuser receiver, I not only evaluate its link-level performance but also discuss how it behaves in a large-scale system. It shows that there is an additional noise enhancement in the proposed detection framework due to the limited number of previous knowledge but its computation complexity and detection delay is lower than most existing multiuser receivers. In a large-scale system with large spreading gain and high SINR, the asymptotic performance of the proposed blind multiuser receivers are close to the conventional ones. Due to the limited space, many detailed proofs of my conclusions are omitted in this blog.

REFERENCE
[1] J. G. Andrews. Interference cancellation for cellular systems: A contemporary overview. IEEE Wireless communications, pages 19–29, April 2005.
[2] S. Wang et al. Towards forward-link interference cancellation. In CDMA Development Group (CDG) Tehnical Forum, San Francisco, CA, April 2006.
[3] S. Verdu. Multiuser Detection. Cambridge University Press, 1998.
[4] U. Madhow. Blind adaptive interference suppression for direct-sequence cdma. Proceedings of the IEEE, 86(10):2049–2069, October 1998.
[5] M. Honig, U. Madhow and S. Verdu. Blind adaptive multiuser detection. IEEE Trans. on Information Theory, 41:944–960, July 1995.
[6] M. Torlak and G. Xu. Blind multiuser channel estimation in asynchronous cdma systems. IEEE Trans. on Signal Processing, 45:137–147, January 1997.
[7] X. Wang and H. V. Poor. Blind multiuser detection: A subspace approach. IEEE Trans. on Information Theory, 44:677-691, March 1998.
[8] X. Zhang and W. Wei. Blind adaptive multiuser detection based on kalman filtering. IEEE Transactions on Signal Processing.
[9] Schmidt R. Multiple emitter location and signal parameter estimation. IEEE Trans. on AP, 34(3):267–280, 1986.
[10] et al. A Hero, H. Messer. Highlights of statistical signal and array processing. IEEE Signal Processing Magazine, 15(5):21–64, September 1998.
[11] B. Yang. Projection approximation subspace tracking. IEEE Trans. on Signal Processing, 43:95–107, January 1995.
[12] D. N. C. Tse and S. V. Hanly. Linear multiuser receivers: Effective interference, effective bandwidth and user capacity. IEEE Trans. on Information Theory, 45:641–657, March 1999.

H.264 Network Abstract Layer Header

2008-09-17T17:43:00.000-07:00

H.264 encoder is composed of two layers, video coding layer (VCL) and network abstraction layer (NAL). VCL translates the video information into bits streams. Since the underlying transportation layers are diversified, NAL maps VCL bitstreams into byte-oriented transportation-layer-friendly and HDLC-like NAL units prior to delivery.

During the mapping of VCL bitstreams to NAL units, at least three operations are done, which are byte alignment, emulation prevention, framing with an additional one-byte NAL unit header. The first byte after a NAL unit start code prefix is the NAL unit header. A NAL unit header consists of three fields.

forbidden_bit (1 bit): may be used to indicate a NAL unit is corrupted or not.
nal_storage_idc (2 bit): signals relative importance, and if the picture is stored in the reference picture buffer.
nal_unit_type (5 bit): signals 1 of 10 different NAL unit types. H.264 defines two additional ranges of NAL unit type values as “Reserved” for future definition of compatible extensions and “Unspecified” for application specific decoders

The field after NAL unit header is NAL unit payload or RBSP payload, which contains either coded slice or additional header or control information, such as sequence or picture parameter set, supplemental enhancement information (SEI), coded data partition, picture delimiter, filter data, etc.

How to Broadcast Multimedia Contents? II Lessons from The Channel

2008-08-25T09:37:00.000-07:00

[How to Broadcast Multimedia Contents? I Introduction]
[How to Broadcast Multimedia Contents? IV Hierarchical Modulation]
[How to Broadcast Multimedia Contents? V Overloaded Transmission and IC]
[How to Broadcast Multimedia Contents? VI Open-Loop MIMO for BCMCS]
[How to Broadcast Multimedia Contents? VII Network Layer or Steam Layer Design]

COST 231 model, which was developed by European COST Action 231, and its variations are the most popular radio propagation model adopted in various standardization bodies, such as 3GPP, 3GPP2 and IEEE. Its modifications include COST 231-Hata Model and COST 231-Walfisch-Ikegami Model. The mathematical formulation of the COST 231-Hata model path loss in dB is

PL = 46.3 + 33.9 logf - 13.82log h_BS - a( h_MS ) + [ 44.9-6.55logh_BS] log d + C

which features a carrier frequency f between 800MHz and 2GHz, an above-neighborhood base station antenna with a height h_BS of 30~300m and a mobile station with an antenna height h_MS of 1~10m. One instance of this path loss model can be shown in Figure 1.

Figure 1. An example of COST 231-Hata urban propagation model.

With Figure 1, we can observe that there are at least two different regions along the channel. The small area close to the base station usually has a high achievable throughput, while the large area on the cell-edge has a low achievable throughput. There two kind of areas are separated in space domain. On the other hand, we alway expect to achieve higher achievable throughput with a good coverage for broadcasting multimedia contents.

Lesson I. Coverage and Throughput Dilemma

Figure 2. The tradeoffs inside the channel

The COST 231 channel model confirms us that there is a well-known trade-off between reception and coverage. In Figure 2, with a 300-meter transmitter antenna, it shows the path-loss changes 0.6560 dB at every 90% coverage change, 1.3894 dB at every 80% coverage change, 2.2209 dB at every 70% coverage change and 3.1807 dB at every 60% coverage change. In general, if you want more coverage, then you may lose some capacity on the cell-edge. Otherwise, you have to shrink your coverage.

Figure 3. Spectral Efficiency and Coverage Tradeoff

Lesson 2. Gaussian Broadcast Channel and Superimposed Transmission

From Figure 2 and 3, it shows you can't get both coverage and cell-edge throughput at the same time. Though we can't break the trade-offs, is there any way to move the trade-off curves in Figure 3 upwards a little bit? Fortunately, information theory has told us there is another option which is worth thinking about. The idea is called superposition precoding or superimposed transmission. The existing of superposition precoding is mostly due to the nonlinearity of Shannon capacity curve. Superimposed transmission suggests splitting one data stream and simultaneously transmitting together instead of orthogonally transmitting the multiple streams, such as in a time-division multiplexing or frequency-division multiplexing fashion. With this way, a higher achievable capacity is possible. This means, if Signals can be sent from two layers, a base layer and a enhancement layer, in which the base layer has the best coverage and the enhancement layer provides additional throughput, the user capacity can increase about 60% with a 3.2 dB path-loss difference between the two layers. If the difference is 1.4dB, the user capacity can increase about 80%.

Figure 4. Capacity Improvement through SPC

Figure 5. User Capacity and Coverage Tradoff

Lesson 3. Fading Channel and Overloaded Transmission

Figure 6. Fading Channel Capacity

It is well-known that homogeneous fading has no effect on CDMA spectral efficiency. However, higher forward-link spectral efficiency is achievable with taking advantage of multiuser diversity. In fact, if an optimum receiver is used and the system loading β=K/N is sufficiently high, even randomly spread CDMA incurs negligible spectral efficiency loss relative to no-spreading. And when β=K/N increases to the infinity, the achievable capacity with and without fading grows to the same ultimate limit. This means, the fading effect vanishes even for the linear receivers, such as matched filter and the MMSE receiver.

Lesson 4. Open-Loop MIMO Channel Capacity

Consider a M-transmit-antenna and N-receive-antenna MIMO link. When both transmitter and receiver know the channel response, it is well-known that the achievable channel capacity C is proportional to min( M, N )log(SNR) + O( 1 ). If only receiver knows the channel response, the achievable channel capacity C is proportional to min( M, N )log(SNR) + O( 1 ). If neither receiver nor transmitter knows the channel response, the achievable channel capacity C is proportional to min( M, N )( 1 - min( M, N )/T ) log(SNR) + O( 1 ). Therefore, in higher SNR region, that transmitter knows the channel may not help much in terms of achievable channel capacity, even though with proper CSI feedback and MIMO precoding at Tx, the required Rx design can be simplified.

Lesson 5. Break The Dilemma with Relay

Figure 8. Extend coverag with relay

How to Broadcast Multimedia Contents? I Introduction

2008-08-08T16:06:00.000-07:00

[How to Broadcast Multimedia Contents? II Lessons from The Channel]
[How to Broadcast Multimedia Contents? IV Hierarchical Modulation]
[How to Broadcast Multimedia Contents? V Overloaded Transmission and IC]
[How to Broadcast Multimedia Contents? VI Open-Loop MIMO for BCMCS]
[How to Broadcast Multimedia Contents? VII Network Layer or Steam Layer Design]

Broadcast multicast service (BCMCS) has increasingly been popular for delivering multimedia content to mobile users. Traditional digital broadcast air interfaces are designed with the tradeoff between maximum achievable rate and intended coverage in mind. The actual rates are usually limited by the maximum transmit power and the worst channel condition so that every user in coverage can reliably receive the services as well as contents of same quality. The users under good reception condition may have no advantage, even if their potential throughputs can be much higher. This happens often, especially on the mobile users whose reception conditions change all the time. And there are rising interests in upgrading existing digital broadcast systems with more services for new users and delivering more quality of service (QoS) options to users with advanced receivers while still guaranteeing existing users' services. Furthermore, recent advances in wideband speech coding, e.g., EVRC-WB, and scalable video coding, e.g., H.264/MPEG-4 AVC, suggest unequal error protection on content delieveries with providing graceful degradation of quality in the presence of increasing packet loss. It is possible for the users in good reception condition have more opportunities to enjoy high quality services while the user with low throughput can still decode the content of basic quality. Many technologies are under investigation for these goals, e.g., rateless coding, hierarchical modulation, multiple-input multiple-output (MIMO), selective retransmission and superposition precoding (SPC). Backward compatibility, implementation complexity and upgrading cost are among the major concerns in upgrading existing systems with additional services. Among those candidates, hierarchical modulation, also called layered modulation, is the most popular one, in which multiple data streams are multiplexed and modulated into one single symbol consisting of base-layer subsymbols and enhancement-layer subsymbols. It has been widely proven and included in various standards, such as DVB-T, MediaFLO, UMB (Ultra Mobile Broadband, a new 3.5th generation mobile network standard developed by 3GPP2), etc., and is under study for DVB-H.

Table 1. Mobile TV standards as of year 2008

Location Based Services for Mobiles II: GPS, Assisted GPS and Network Assisted GPS

2008-06-20T13:32:00.000-07:00

[Location Based Service for Mobiles I: Technologies and Standards]

GPS - Global Positioning System

Figure 1. GPS System Archiecture: Space Segment, Control Segment and User Segment

GPS is a Global Navigation Satellite System for determining the positions of receivers using signals broadcast by satellites. It was developed and operated by US government to enhance the effectiveness of allied and US military forces. The first experimental Block-I GPS satellite was launched in 1978. Since 1983, GPS has become an aid to civilian navigation worldwide, and a useful tool for survey, commerce, and scientific uses. As of September 2007, there are 31 actively broadcasting satellites in the GPS constellation. Satellites orbit 20,163 kilometers above the earth at 3.87 km/s. 6 orbital planes, each with 4+ satellites. Typically 6 to 12 satellites are visible from any place on the earth. GPS based positioning is playing a critical role in modern location based services.

Figure 2. The Block Digrame of GPS Positioning

A GPS receiver measures approximate distance to 3 or more satellites. It measures the time required for signal to get from the satellite to the receiver. It then calculates the distance and obtains satellite positions from satellite broadcasts. Finally it calculates the position using trilateration. During the positioning, it corrects for errors to improve accuracy with calibrating the clock bias or applying differential correction. It also corrects deliberate noise, such as selective availability and caliberates variable ionospheric and tropospheric propagation delays.

All GPS satellites transmit on L1 and L2 frequencies.

Each satellite uses different ranging codes: C/A code and P-code.
L1 band is for civilian use.

The C/A code (coarse/acquisition code) is modulated onto the L1 carrier only, while the P-code (precise code) is modulated onto both the L1 and L2 carriers.
The C/A code is less precise and less complex than the P-code and available to all users.

The P-code is intended for military uses and is added to both L1 and L2.

Figure 3. GPS Frame Structure and Navigation Data

TLM – Telemetry: 30 bits, sent at the beginning of each frame.

It is used for data synchronization and satellite maintenance.
They are usually constant for any one satellite for a long period of time.

HOW – Handover Word: 30 bits, sent after TLM.

It indicates the time at the beginning of the next subframe.
It also contains a sub-frame ID, some flags and parity bits.

Ephemeris: It is sent in each frame by each satellite.

It may take the GPS receiver up to 30 seconds to acquire Ephemeris.

Almanac: It is spread out over all 25 frames of the message.

For receiving the complete Almanac, the GPS receiver may need about 12.5 minutes.

Figure 4. GPS Positioning Error Sources

Assisted Global Positioning System

Assisted GPS (A-GPS) with assistance server were first come out by Bell Labs and later developed to enhance the positioning performance of a GPS receiver and satisfy US FCC's E911 mandate.

Figure 5. The Block Diagram of A-GPS System

GPS assistance server can increase the capability of a stand-alone GPS receiver. It can roughly locate mobiles along by itself. It can supply more GPS orbital data to the mobile. It has better knowledge of atmosphere conditions and other errors as well as better augmentation capability. With the GPS assistance server, A-GPS helps improve positioning in terms of

Location accuracy: the positioning error.
Yield: the positioning success rate.
Time to fix: the time for positioning.
Battery consumption: power consumption for positioning.
Mobile cost

Network Assisted Global Positioning System

Figure 6. The Concept of N-GPS

With N-GPS, key GPS assistance is provided through control plan instead of user plan. No additional data channel setup overhead required. No additional layer-3 authentication or access control required. No roaming issue. It is more reliable than layer-3 A-GPS approaches and more efficient since the assistance data is periodically broadcasted. It is fully compatiable with most existing A-GPS receivers.

Figure 7. An Application Scenario of N-GPS

Table 1: A Comparison fo GPS, N-GPS and A-GPS

Location Based Services for Mobiles I: Technologies and Standards

2008-06-08T09:24:00.000-07:00

[Tutorial in IEEE International Conference on Communications (ICC) 2008]

Location based services (LBS) for mobile are the services supported by cellular networks for providing mobile users with various location sensitive applications such as E911, Friendfinder, personalized advertisement, etc. LBS accelerate the convergence of 3C (computer, communication and consumer electronics). One aspect of LBS market is the rapid growth of GPS market, which is predicted to reach $28.9 billion by 2010 by GPS World. It is believed that LBS is bringing huge revenue opportunities for wireless network operators and service providers. The driving force behind of the growth of LBS market includes regulator’s mandates, the development of more efficient location technologies and the expanding of LBS from network operator to third service provider.

In this tutorial, the state of art of mobile location based services (LBS) will be explored in terms of technologies, standards and implementations. There are five major parts in this proposed tutorial. Within the first part, an introduction to LBS is presented along with an overview of the growing LBS market. Two examples of LBS, E911 and telematics, are emphasized. In the second part, LBS from a network operator perspective is discussed with a survey of wireless location technologies, the exploration of location management in cellular network, and LBS standards activities. The architecture and operation of the network-dependent LBS control plane of cdma2000 and UMTS networks are reviewed, respectively. In the third part, the IP-based LBS user plane is discussed from a service provide perspective. An overview of the related standards by OMA and 3GPP2 is given and the principles of LBS user plane are illustrated from multiple application scenarios. Finally, the further works and standard activities for LBS are presented.

In summary, this tutorial is intended to provide a comprehensive overview of mobile LBS for a wide array of audiences, including LBS services providers, application developers, marketing managers and system researchers, etc. It includes not only the background information but also standards activities.