A Novel Low-Complexity Massive MIMO Detector with Near-Optimum Performance

In massive multiple-input multiple-output (MIMO) detection, likelihood ascent search (LAS) is well-known for its near-optimum performance with low complexity. It employs gradient descent to enhance the performance of suboptimal MIMO detectors, specifically minimum mean-square error (MMSE). In this paper, we introduce several new techniques to improve the MMSE-based LAS in terms of either complexity or performance. The MMSE is first replaced with optimized coordinate descent algorithm (OCD), which performs near MMSE with lower complexity. Then, the conventional OCD and LAS are reformulated and approximated to better reuse the computation for gradient descent, which is required in both algorithms. Besides, we also optimize the search strategy of LAS, leading to the improvement in both complexity and performance. The proposed detector, modulation-based successive gradient descent (MB-SGD) algorithm, outperforms MMSE-LAS and the latest low-complexity near-optimum detector in terms of either complexity or performance for 64×8 and 128×8 MIMO systems under 256-QAM. The corresponding architecture for a 128 × 8 256-QAM MIMO system has 75.8% lower latency, 2.02 × higher area efficiency, and 0.3 dB gain when implemented on a ×Xilinx Virtex-7 FPGA compared to OCD’s.