Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
Journal of Multivariate Analysis
Multiuser Detection
Fundamentals of wireless communication
Fundamentals of wireless communication
Joint beamforming for multiaccess MIMO systems with finite rate feedback
IEEE Transactions on Wireless Communications
Spectral efficiency of CDMA with random spreading
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signature optimization for CDMA with limited feedback
IEEE Transactions on Information Theory
How many users should be turned on in a multi-antenna broadcast channel?
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Information Theory
Training-feedback tradeoff in closed-loop synchronous DS-CDMA systems with adaptive user sequences
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
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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 "match" 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.