Channel Capacity Maximization in Multiuser Large Scale MIMO-Based Cognitive Networks

This paper proposes an adaptive multiuser Multiple Input Multiple Output-Space Division Multiplexing Access (MU-MIMO-SDMA) technique for uplink access in broadband wireless cognitive networks with multiple primary users (PUs) and secondary users (SUs) sharing the same spectrum. The proposed algorithm uses gradient search of the channel capacity to seek, iteratively, the optimal transmit weight vectors that maximize the MIMO channel capacity for each cognitive user, while controlling the interference levels to the PUs. Simulation results show that the symbol error rate (SER) performance and the capacity of cognitive MIMO systems using the proposed adaptive MIMO-SDMA algorithm is substantially higher than the one based on conventional approaches such as eigen-beamforming. The paper also investigates the performance of large scale MIMO-SDMA system and shows that as the number of base station antennas becomes larger, the constraints imposed by the primary network could be relaxed and the performance of SUs are improved without harming the PUs performance.