Adaptive decorrelating detectors for CDMA systems

Multi-user detection allows for the efficient use of bandwidth in Code-Division Multiple-Access (CDMA) channels through mitigation of near-far effects and multiple-access noise limitations. Due to its inherent noise and multipath immunity, CDMA multi-access is being considered as a platform for personal communication systems (PCS). As CDMA based digital communication networks proliferate, the need to determine the presence of a new user and integrate knowledge of this new user into the detection scheme becomes more important. The decorrelating detector is a linear multi-user detector that is asymptotically optimal in terms of near far resistance; however, in the presence of a new unknown user, performance of the decorrelator is severely degraded. Adaptive decorrelators are constructed which adaptively augment an existing conventional decorrelator to demodulate a new active user in addition to existing users. Several likelihood ratio based schemes are employed. Both synchronous and asynchronous communication are investigated.

[1]  Karen W. Halford,et al.  USER IDENTIFICATION AND MULTIUSER DETECTION OF L OUT OF K USERS IN A CDMA SYSTEM , 1994 .

[2]  D.L. Schilling,et al.  Spread spectrum goes commercial , 1990, IEEE Spectrum.

[3]  Urbashi Mitra,et al.  Analysis of an adaptive decorrelating detector for synchronous CDMA channels , 1993, IEEE Trans. Commun..

[4]  Sergio Verdú,et al.  Linear multiuser detectors for synchronous code-division multiple-access channels , 1989, IEEE Trans. Inf. Theory.

[5]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[6]  H. Vincent Poor,et al.  An introduction to signal detection and estimation (2nd ed.) , 1994 .

[7]  J. Andel.  Sequential Analysis , 2022, The SAGE Encyclopedia of Research Design.

[8]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[9]  A. Stuart.  Equally Correlated Variates and the Multinormal Integral , 1958 .

[10]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[11]  Demetrios Kazakos.  Asymptotic error probability expressions for multihypothesis testing using multisensor data , 1991, IEEE Trans. Syst. Man Cybern..

[12]  D. Luenberger.  Optimization by Vector Space Methods , 1968 .

[13]  Donald L. Schilling,et al.  Spread spectrum for sale , 1990 .

[14]  M.B. Pursley,et al.  Crosscorrelation properties of pseudorandom and related sequences , 1980, Proceedings of the IEEE.

[15]  Sergio Verdú,et al.  Minimum probability of error for asynchronous Gaussian multiple-access channels , 1986, IEEE Trans. Inf. Theory.

[16]  Alston S. Householder,et al.  The Theory of Matrices in Numerical Analysis , 1964 .

[17]  U. Mitra,et al.  Activity detection in a multi-user environment , 1996, Wirel. Pers. Commun..

[18]  Sergio Verdú,et al.  Near-far resistance of multiuser detectors in asynchronous channels , 1990, IEEE Trans. Commun..

[19]  D. Slepian.  The one-sided barrier problem for Gaussian noise , 1962 .

[20]  Urbashi Mitra,et al.  Detection of spread-spectrum signals in a multi-user environment , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.