Optimal Power Allocation with Channel Inversion Regularization-Based Precoding for MIMO Broadcast Channels

Abstract

Zero-forcing (ZF) precoding scheme can achieve the asymptotic sum capacity as dirty-paper coding (DPC) in multiple-input multiple-output broadcast (MIMO-BC) channel when the number of users, , approaches infinity. However, the gap between ZF and DPC is not negligible in a practical range of , that is, . The capacity loss is partly due to the excessive transmission power penalty incurred by ZF when the channel matrix of the selected user subset is poorly conditioned. To avoid this power penalty, we propose to use a variation of ZF, channel inversion regularization (CIR), as a precoding scheme in MIMO-BC channels. But, unlike the interference-free ZF, the problem of maximizing sum-rate capacity using CIR precoding becomes nonconvex, which cannot be solved by water-filling strategy. Thus, we propose an efficient algorithm based on gradient projection (GP) as the optimal power allocation strategy for selected users, and show that the proposed CIR precoding scheme can achieve asymptotically the optimum sum-rate of the DPC strategy. Moreover, simulation results show that the CIR precoding scheme with the proposed optimal power allocation scheme achieves better sum-rate performance than ZF for a wide range of .

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