MU-MIMO with Channel Statistics-Based Codebooks in Spatially Correlated Channels

We investigate the performance of limited feedback zero forcing beamforming-based MU-MIMO in correlated channels when users have the opportunity to adapt their codebooks as a function of their own channel statistics (CDIT). An upper bound on the rate loss is derived in terms of the amount of feedback and the statistics of the channel. In order to maintain a constant rate loss with respect to the perfect CSIT case, it is shown that the number of feedback bits should scale linearly with the SNR (in dB) with a slope proportional to the rank of the user transmit correlation matrix and logarithmically with the ratio of the second to the first largest eigenvalue of the transmit correlation matrix. Simulation results confirm analytical derivations and suggest that a channel statistics-based codebook is a reasonable simple approach to boost the throughput in correlated channels while incurring only a small overhead.