Channel capacity maximization in MIMO-SDMA based cognitive networks

This paper proposes an adaptive multi-user Multiple Input Multiple Output (MIMO)-Space Division Multiplexing Access (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 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. On the other hand, it is shown that stronger interference power constraint has a considerable impact on the channel capacity.