Convex optimization based multiuser detection for uplink large-scale MIMO under low-resolution quantization

Abstract

In large-scale Multiple-Input-Multiple-Output, the Base Station (BS) is equipped with a large-size antenna array containing tens even hundreds Radio Frequency channels. If so many RF front-ends adopt the classical receivers, their cost and power consumption during the receiving mode would increase quickly. Therefore, we exploit low-resolution Analog-to-Digital-Convertor to design low-power and low-cost software defined radio receivers for the BS. However, the new problem is how the BS fulfills channel estimation and Multiuser Detection (MUD) under low-resolution quantization. In this paper, first, least square method is used for channel estimation, and a robust Maximum Likelihood (ML) MUD problem is constructed to take into account the channel estimation errors. Second, an iterative multiuser detector is constructed by relaxing the ML MUD problem as a convex optimization problem and then solving the convex problem through the nonmonotone spectral projected gradient method. Compared with the Minimum Mean Square Error (MMSE) detector, the proposed detector has lower computational complexity, and is more suitable for hardware implementation. Simulation results show that it also outperforms MMSE.