An Efficient Massive MIMO Detector Based on Approximate Expectation Propagation

Abstract

Among expectation propagation (EP)-based massive multiple-input–multiple-output (MIMO) detection algorithms, EP with weighted Neumann-series approximation (EPA-wNSA) has the lowest computational complexity while requiring many iterations to guarantee the detection performance, which severely limits the throughput of hardware implementations. Through the joint optimization of algorithm and hardware architecture, we propose an EP-based detector with higher throughput and area efficiency. First, the second-order Richardson iteration (SORI) algorithm is employed to replace the wNSA algorithm for higher convergence speed. Then three algorithmic transformations are proposed to minimize the overall complexity. Simulation results show that the proposed EPA-SORI algorithm requires much fewer iterations to achieve comparable or even better detection performance compared with EPA-wNSA. Furthermore, an efficient detector architecture is delicately designed by incorporating multiple optimization methods, such as reverse data flow, advanced addition, and rounding cells. Implemented with the Taiwan Semiconductor Manufacturing Company (TSMC) 28-nm CMOS technology, the proposed detector has $2.2 \times $ higher throughput than the state-of-the-art EP-based detector.