A Low-Complexity Soft-Output Massive MIMO Detector With Near-Optimum Performance

In massive multiple-input multiple-output (MIMO) detection, the likelihood ascent search (LAS) algorithm is well-known for its near-optimum performance and low complexity. It employs gradient descent to enhance the performance of suboptimal MIMO detectors, specifically the minimum mean-square error (MMSE) algorithm. In this paper, we introduce several techniques to improve the MMSE-based LAS (MMSE-LAS) algorithm in terms of both complexity and performance. To reduce complexity, the MMSE is first replaced with the low-complexity optimized coordinate descent (OCD) algorithm at the cost of negligible performance loss. Then, the conventional OCD and LAS algorithms are optimized for better computation reuse. Besides, we derive a new soft-output computation formula for LAS to improve the coded performance. The proposed modulation-based successive gradient descent (MB-SGD) detector outperforms MMSE-LAS and the latest work in terms of either complexity or performance for <tex-math notation="LaTeX">64× 8 </tex-math> and <tex-math notation="LaTeX">128× 8 </tex-math> LDPC-coded MIMO systems with multiple modulations from QPSK to 256-QAM. The corresponding architecture for a <tex-math notation="LaTeX">128× 8 </tex-math> coded MIMO system supporting multiple modulations is implemented on a Xilinx Virtex-7 FPGA and with TSMC 28-nm CMOS technology, exhibiting 74.5% lower latency and 0.24 dB gain compared to OCD on FPGA, and also achieving <tex-math notation="LaTeX">14.59× </tex-math> energy efficiency and <tex-math notation="LaTeX">2.04× </tex-math> area efficiency over the state-of-the-art implementation on ASIC.