Interference Cancellation: IV A Blind Receiver Design Perspective
[ Interference Cancellation. I. A Short Overview of Multiusr Detection ]
[ Interference Cancellation: II. A Conventional Receiver Design Perspective ]
[ Interference Cancellation: III. A Signal Subspace Perspective ]
S = [A1s1 r1 r2 ... rM−1 ]= SA[ e1 B] + N = SAB + N
where {rm : m = 1, 2, ... , M−1} are (M − 1) previously received symbols, el is a K × 1 identity vector with a 1 as the lth element and 0’s as the rest, the K × 1 vectors bm denotes the data sent by all K users with rm and the data matrix B is
B = [ b1 b2 ... bM−1 ] = [g FH ]H
[ 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 {sk : k ≠ 1} or the signal subspace matrix Us are unknown beforehand. In most blind multiuser receivers, either the signal signature matrix S and the subspace transform matrix ะค 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 ... rM−1 ]= SA[ e1 B] + N = SAB + N
where {rm : m = 1, 2, ... , M−1} are (M − 1) previously received symbols, el is a K × 1 identity vector with a 1 as the lth element and 0’s as the rest, the K × 1 vectors bm denotes the data sent by all K users with rm and the data matrix B is
B = [ b1 b2 ... bM−1 ] = [g FH ]H
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| The proposed blind receiver design framework |
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| A comparison between the blind receiver design framework and other detection approaches |
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| A performance comparison of various multiuser receivers |
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