Abstract
This article considers using a 1-b spatial equalization matrix to enable low-power and high-throughput hardware processing in massive multiuser multiple-input--multiple-output (MIMO) uplink systems with all-digital base station architectures. We aim to design 1-b spatial equalization matrix coefficients that minimize the postequalization mean squared error that leads to a nonconvex problem. Known solutions based on semidefinite relaxation (SDR) and forward–backward splitting (FBS) are proposed to solve the 1-b spatial equalization matrix design problem. The SDR-based algorithm can achieve excellent bit error rate (BER) performance. However, its high complexity limits its use in large-scale system configurations. The FBS-based algorithm contains several predetermined tuning parameters that are scenario-dependent and nontrivial to obtain. Moreover, the performance of the FBS-based algorithm still requires improvement. On the basis of these considerations, we propose two algorithms for solving the 1-b spatial equalization matrix design problem. The first algorithm reduces the performance gap between the unquantized linear minimum mean squared error equalizer and the 1-b SDR-assisted equalizer while maintaining a reasonable complexity; the second algorithm balances the tradeoff between complexity and performance. Simulation results show that the proposed algorithms can achieve excellent BER performance compared with state-of-the-art methods.
Published Version
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