Fast iterative WSVT algorithm in WNN minimization problem for multiuser massive MIMO channel estimation

Abstract

SummaryIn this paper, channel estimation for multiuser massive MIMO system is addressed for the scenario, where the number of scatterers is small compared to the base station antennas and single antenna users in the cell. If the number of scatterers is limited, then the corresponding angle of arrivals are finite. Moreover, if all the users share the same angle of arrivals, then the correlation among the channel vector increases.Thus, the high-dimensional channel is approximated to the low-rank matrix. This rank minimization problem is formulated as the weighted nuclear norm problem and is estimated using iterative weighted singular value thresholding (IWSVT) algorithm. To increase the convergence rate, fast IWSVT algorithm is proposed and the performance is measured based on mean square error and uplink and downlink achievable sum-rate. The simulation study shows that the proposed weighted nuclear norm minimization method with fast IWSVT algorithm performs better than the conventional least square and the nuclear norm minimization method for various finite scatterers in different signal-to-noise ratio levels.

Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers

“…For MU-MIMO uplink orthogonal frequency division multiple access (OFDMA) video transmission systems, the authors of [3] propose an average peak-signal-to-noise-ratio (PSNR) optimized cross-layer resource allocation and user grouping scheme, in which zero-forcing MU-MIMO detector is considered. When the number of scatterers is small compared to the base station antenna and single antenna users in the cell, the authors of [4] proposes a fast iterative weighted singular value thresholding (WSVT) algorithm to obtain a more accurate channel estimate.It is worth emphasizing that, when the elements of the BS antenna array become large, the column-vectors of the propagation matrix from BS to users tend to be orthogonal [5,6]. Considering a ZF detector, an approximate distribution for the received SINR and an approximated symbol error probability (SEP) were explored [7].…”

Channel estimation based on learning automata for OFDM systems

“…In addition, the performance of data detection algorithms mainly depends on crucial information accuracy. Various techniques including blind and semi-blind methods [3][4][5][6][7][8] and pilot-based methods [9][10][11][12][13][14][15][16][17][18][19] have been represented for channel estimation. Most of the blind methods intrinsically have low convergence rate and weak performance.…”