Shu (Marloon) Wang

[ 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

[ Interference Cancellation. I. A Short Overview of Multiusr Detection ] [ Interference Cancellation: II. A Conventional Receiver Design Perspective ] [ Interference Cancellation: IV. A Blind Receiver Design Perspective ] 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. A