Application of distributed signal detection to multiuser communication systems

Distributed signal detection is extended to multiuser scenarios. Optimum decision rules for cooperating receivers are found for the jointly optimum criterion and the individually optimum criterion. Numerical results show that distributed systems achieve nearly centralized performance using only one or two-bit descriptions of the preliminary decisions. Moreover, distributed signal detection can be helpful in lessening the severity of the near-far problem when full multiuser detection is impractical. To further reduce the complexity, a suboptimum approach which employs linear multiuser detectors at its front end is investigated. The investigated suboptimum approach is shown to achieve considerable improvement over an alternate suboptimum approach that attempts to minimize distortion.

[1]  Pramod K. Varshney,et al.  Distributed detection with multiple sensors I. Fundamentals , 1997, Proc. IEEE.

[2]  Upamanyu Madhow Blind adaptive interference suppression for direct-sequence CDMA , 1998 .

[3]  H. Vincent Poor,et al.  Blind Multiuser Detection: A Subspace Approach , 1998, IEEE Trans. Inf. Theory.

[4]  Sergio Verdu,et al.  Multiuser Detection , 1998 .

[5]  Rick S. Blum,et al.  Distributed detection with multiple sensors I. Advanced topics , 1997, Proc. IEEE.

[6]  R.S. Blum,et al.  Distributed multiuser detection , 1999, Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers (Cat. No.CH37020).

[7]  Wei Chang,et al.  Performance and geometric interpretation for decision fusion with memory , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[8]  Pramod K. Varshney,et al.  On distributed signal detection with multiple local free parameters , 1999 .

[9]  Pramod K. Varshney,et al.  Multiuser detection with cell diversity for DS/CDMA systems , 2000, 2000 IEEE Wireless Communications and Networking Conference. Conference Record (Cat. No.00TH8540).

[10]  R. Viswanathan,et al.  Multilevel quantisation and fusion scheme for the decentralised detection of an unknown signal , 1994 .

[11]  Zygmunt J. Haas,et al.  The multiply-detected macrodiversity scheme for wireless cellular systems , 1998 .

[12]  Rick S. Blum,et al.  The good, bad and ugly: distributed detection of a known signal in dependent Gaussian noise , 2000, IEEE Trans. Signal Process..

[13]  D. Kazakos,et al.  On-Line Threshold Learning for Neyman-Pearson Distributed Detection , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[14]  Rick S. Blum Distributed Detection for Diversity Reception of Fading Signals in Noise , 1999, IEEE Trans. Inf. Theory.

[15]  Nirwan Ansari,et al.  Performance analysis and realization of decision fusion for macroscopic diversity in cellular wireless systems , 1997, GLOBECOM 97. IEEE Global Telecommunications Conference. Conference Record.

[16]  D. Warren,et al.  Optimum quantization for detector fusion: some proofs, examples, and pathology , 1999 .

[17]  Rick S. Blum Distributed reception of fading signals in noise , 1995, Proceedings of 1995 IEEE International Symposium on Information Theory.

[18]  Pramod K. Varshney,et al.  Distributed Detection and Data Fusion , 1996 .