An Efficient Approximate Expectation Propagation Detector With Block-Diagonal Neumann-Series

Expectation propagation (EP) achieves near-optimal performance for large-scale multiple-input multiple-output (L-MIMO) detection, however, at the expense of unaffordable matrix inversions. To tackle the issue, several low-complexity EP detectors have been proposed. However, they all fail to exploit the properties of channel matrices, thus resulting in unsatisfactory performance in non-ideal scenarios. To this end, in this paper, a block-diagonal Neumann-series-based expectation propagation approximation (BD-NS-EPA) algorithm is proposed, which is applicable for both ideal uncorrelated channels and the correlated channels with multiple-antenna user equipment system. First, a block-diagonal-based Neumann iteration is employed, which skillfully exerts the main information of the channels while reducing computational cost. An adjustable sorting message updating scheme then is introduced to reduce the update of redundant nodes during iterations. Numerical results show that, for $128\times 32$ MIMO with the non-ideal channel, the proposed algorithm exhibits 0.3 dB away from the original EP when bit error-rate (BER) $=10^{-3}$ , at the cost of mere 3% normalized complexity. The implementation results on SMIC 65-nm CMOS technology suggest that the proposed detector can achieve 1.252 Gbps/W and 0.275 Mbps/kGE hardware efficiency, further demonstrating that the proposed detectors can achieve a good trade-off between error-rate performance and hardware efficiency.