Title: Residual-Based Detections and Unified Architecture for Massive MIMO Uplink,
Abstract: 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.