The Effect of Finite Rate Feedback on CDMA Signature Optimization and MIMO Beamforming Vector Selection

We analyze the effect of finite rate feedback on code-division multiple-access (CDMA) signature optimization and multiple-input multiple-output (MIMO) beamforming vector selection. In CDMA signature optimization, for a particular user, the receiver selects a signature vector from a codebook to best avoid interference from other users, and then feeds the corresponding index back to the specified user. For MIMO beamforming vector selection, the receiver chooses a beamforming vector from a given codebook to maximize the instantaneous information rate, and feeds back the corresponding index to the transmitter. These two problems are dual: both can be modeled as selecting a unit norm vector from a finite size codebook to ldquomatchrdquo a randomly generated Gaussian matrix. Assuming that the feedback link is rate limited, our main result is an exact asymptotic performance formula where the length of the signature/beamforming vector, the dimensions of interference/channel matrix, and the feedback rate approach infinity with constant ratios. The proof rests on the large deviations of the underlying random matrix ensemble. Further, we show that random codebooks generated from the isotropic distribution are asymptotically optimal not only on average, but also in probability.

[1]  M. Ledoux DIFFERENTIAL OPERATORS AND SPECTRAL DISTRIBUTIONS OF INVARIANT ENSEMBLES FROM THE CLASSICAL ORTHOGONAL POLYNOMIALS: THE DISCRETE CASE , 2019 .

[2]  A. James Distributions of Matrix Variates and Latent Roots Derived from Normal Samples , 1964 .

[3]  Michael L. Honig,et al.  Signature optimization for CDMA with limited feedback , 2005, IEEE Transactions on Information Theory.

[4]  A. Dembo,et al.  Large Deviation Techniques and Applications. , 1994 .

[5]  Youjian Liu,et al.  Performance Analysis of CDMA Signature Optimization with Finite Rate Feedback , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[6]  A. James Normal Multivariate Analysis and the Orthogonal Group , 1954 .

[7]  Z. Bai,et al.  On the limit of the largest eigenvalue of the large dimensional sample covariance matrix , 1988 .

[8]  W SilversteinJack Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices , 1995 .

[9]  Youjian Liu,et al.  Joint beamforming for multiaccess MIMO systems with finite rate feedback , 2009, IEEE Transactions on Wireless Communications.

[10]  Noureddine El Karoui,et al.  Concentration of measure and spectra of random matrices: Applications to correlation matrices, elliptical distributions and beyond , 2009, 0912.1950.

[11]  Z. Bai,et al.  Limit of the smallest eigenvalue of a large dimensional sample covariance matrix , 1993 .

[12]  J. W. Silverstein Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices , 1995 .

[13]  A. Guionnet,et al.  CONCENTRATION OF THE SPECTRAL MEASURE FOR LARGE MATRICES , 2000 .

[14]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[15]  Vincent K. N. Lau,et al.  On the Information Rate of MIMO Systems With Finite Rate Channel State Feedback Using Beamforming and Power On/Off Strategy , 2006, IEEE Transactions on Information Theory.

[16]  N. S. Barnett,et al.  Private communication , 1969 .

[17]  Shlomo Shamai,et al.  Spectral Efficiency of CDMA with Random Spreading , 1999, IEEE Trans. Inf. Theory.

[18]  Vincent K. N. Lau,et al.  Capacity of memoryless channels and block-fading channels with designable cardinality-constrained channel state feedback , 2004, IEEE Transactions on Information Theory.

[19]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[20]  A. Edelman Eigenvalues and condition numbers of random matrices , 1988 .

[21]  Michel Ledoux,et al.  Differential Operators and Spectral Distributions of Invariant Ensembles from the Classical Orthogonal Polynomials. The Continuous Case , 2004 .

[22]  Michael L. Honig,et al.  Benefits of limited feedback for wireless channels , 2003 .

[23]  R. Muirhead Aspects of Multivariate Statistical Theory , 1982, Wiley Series in Probability and Statistics.

[24]  Bernard Fino,et al.  Multiuser detection: , 1999, Ann. des Télécommunications.

[25]  M.L. Honig,et al.  Achievable rates for MIMO fading channels with limited feedback , 2004, Eighth IEEE International Symposium on Spread Spectrum Techniques and Applications - Programme and Book of Abstracts (IEEE Cat. No.04TH8738).