Smart Sorting in Massive MIMO Detection

In this paper, we proposed a discrete sorting optimization approach for uplink channel of Massive Multiple Input, Multiple Output (MIMO) system. The algorithm includes users (UEs) sorting before QR decomposition (QRD) and sorting reduced (SR) K-best detector for 48x64 MIMO uncoded systems. Simulation results show that detector losses is about 1dB to a Maximum Likelihood (ML) detection in low detector complexity. The UEs sorting is required to sort diagonal elements of the R matrix in ascending order to avoid error propagation in multiuser (MU) scenario. Fast and low complexity method of online discrete optimization is used to find the loss function minimum. Sorting tracking is proposed, so that the pre-sorted interpolated R matrix is used for further sorting optimization, resulting in low sorting complexity. The proposed sorting demonstrates huge performance gain compare to a common power-based one. Simulation results in SG QuaDRiGa channel are presented. SR-K-best detector is a variant of K-best detector. The SR K -best with (K,S,p) parameters results in significant losses in scenarios with high correlated users, therefore we proposed a new structure (K,S,p,v,q) of the SR-K-best algorithm and a new discrete optimization method to increase performance. Discrete stochastic optimization was done offline in QuaDRiGa channel to find optimal (K,S,p,v,q) parameters for fixed detector structure.