Convergence analysis of chip- and fractionally spaced LMS adaptive multiuser CDMA detectors

This paper analyzes the convergence behavior of the least mean square (LMS) filter when used in an adaptive code division multiple access (CDMA) detector consisting of a tapped delay line with adjustable tap weights. The sampling rate may be equal to or higher than the chip rate, and these correspond to chip-spaced (CS) and fractionally spaced (FS) detection, respectively. It is shown that CS and FS detectors with the same time-span exhibit identical convergence behavior if the baseband received signal is strictly bandlimited to half the chip rate. Even in the practical case when this condition is not met, deviations from this observation are imperceptible unless the initial tap-weight vector gives an extremely large mean squared error (MSE). This phenomenon is carefully explained with reference to the eigenvalues of the correlation matrix when the input signal is not perfectly bandlimited. The inadequacy of the eigenvalue spread of the tap-input correlation matrix as an indicator of the transient behavior and the influence of the initial tap weight vector on convergence speed are highlighted. Specifically, a initialization within the signal subspace or to the origin leads to very much faster convergence compared with initialization in the a noise subspace.

[1]  Scott L. Miller An adaptive direct-sequence code-division multiple-access receiver for multiuser interference rejection , 1995, IEEE Trans. Commun..

[2]  C. D. Meyer,et al.  Generalized inverses of linear transformations , 1979 .

[3]  G. Ungerboeck,et al.  Fractional Tap-Spacing Equalizer and Consequences for Clock Recovery in Data Modems , 1976, IEEE Trans. Commun..

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

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

[6]  R. D. Gitlin,et al.  Fractionally-spaced equalization: An improved digital transversal equalizer , 1981, The Bell System Technical Journal.

[7]  Simon Haykin,et al.  Adaptive filter theory (2nd ed.) , 1991 .

[8]  Scott L. Miller Training analysis of adaptive interference suppression for direct-sequence code-division multiple-access systems , 1996, IEEE Trans. Commun..

[9]  Sumit Roy,et al.  Adaptive filters in multiuser (MU) CDMA detection , 1998, Wirel. Networks.

[10]  Branka Vucetic,et al.  Adaptive receiver structures for asynchronous CDMA systems , 1994, IEEE J. Sel. Areas Commun..

[11]  Paul Dean Alexander,et al.  A Linear Model for CDMA Signals Received with Multiple Antennas Over Multipath Fading Channels , 1999 .

[12]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[13]  S. R. Searle,et al.  Matrix Algebra Useful for Statistics , 1982 .

[14]  Saeed Gazor,et al.  Selection of orthonormal transforms for improving the performance of the transform domain normalised LMS algorithm , 1992 .

[15]  Upamanyu Madhow,et al.  MMSE interference suppression for direct-sequence spread-spectrum CDMA , 1994, IEEE Trans. Commun..

[16]  Upamanyu Madhow,et al.  Blind adaptive multiuser detection , 1995, IEEE Trans. Inf. Theory.

[17]  B. Farhang-Boroujeny,et al.  Adaptive Filters: Theory and Applications , 1999 .

[18]  H. Vincent Poor,et al.  Code-aided interference suppression for DS/CDMA communications. II. Parallel blind adaptive implementations , 1997, IEEE Trans. Commun..

[19]  R. Gitlin,et al.  The tap-leakage algorithm: An algorithm for the stable operation of a digitally implemented, fractionally spaced adaptive equalizer , 1982 .

[20]  Craig K. Rushforth,et al.  A Family of Suboptimum Detectors for Coherent Multiuser Communications , 1990, IEEE J. Sel. Areas Commun..