Residual-Based Detections and Unified Architecture for Massive MIMO Uplink

Massive multiple-input multiple-output (M-MIMO) technique brings better energy efficiency and coverage but higher computational complexity than small-scale MIMO. For linear detections such as minimum mean square error (MMSE), prohibitive complexity lies in solving large-scale linear equations. For a better trade-off between bit-error-rate (BER) performance and computational complexity, iterative linear algorithms like conjugate gradient (CG) have been applied and have shown their feasibility in recent years. In this paper, residual-based detection (RBD) algorithms are proposed for M-MIMO detection, including minimal residual (MINRES) algorithm, generalized minimal residual (GMRES) algorithm, and conjugate residual (CR) algorithm. RBD algorithms focus on the minimization of residual norm per iteration, whereas most existing algorithms focus on the approximation of exact signal. Numerical results have shown that, for $64$-QAM $128\times 8$ MIMO, RBD algorithms are only $0.13$ dB away from the exact matrix inversion method when BER$=10^{-4}$. Stability of RBD algorithms has also been verified in various correlation conditions. Complexity comparison has shown that, CR algorithm require $87\%$ less complexity than the traditional method for $128\times 60$ MIMO. The unified hardware architecture is proposed with flexibility, which guarantees a low-complexity implementation for a family of RBD M-MIMO detectors.