Combined Transceiver Optimization for Uplink Multiuser MIMO with Limited CSI

Abstract

Joint precoder and decoder optimization is considered for uplink multiuser multiple-input multiple-output (MU-MIMO) systems with limited channel state information (CSI) at both the transmitters and receivers. Instead of counting on complex iterative-based algorithms, an efficient and noniterative QR-based linear transceiver pair design is employed. In addition, an equal power distribution (EPD) scheme is applied to adjust transmit power allocation of each mobile station (MS) between its symbols under the total transmit-power constraint. Simulations are conducted to provide a comparative evaluation of the proposed QR-EPD algorithm with other transceiver designs on the sum mean-squared error (SMSE) and the averaged bit-error-rate (BER) performance.

Design of Robust Transceiver for Precoded Uplink MU-MIMO Transmission in Limited Feedback System

“…5a shows that the proposed RMV-based and MVbased detection schemes have better detection performance than conventional SVD-assisted detection schemes in [6], due to the optimum design of adaptive and precoder matrices jointly in (7) and (21) to resist the effect of MUI and noise. Figure 5a also shows that the RMVbased scheme is an unbiased design due to the involvement of the same BER performance compared with the MV-based scheme depicted in (26). For imperfect CSI estimation, Fig.…”

“…The LS technique is considered for the CSI estimation and the input SNR is defined as P/σ 2 v . The robust design with performance analysis is also examined on theoretic analysis (i.e., theoretic) and computer simulation analysis (i.e., simulation) [24][25][26]. Furthermore, we consider the proposed schemes with N t , M r ∈ {4, 6}, N 2 ∈ {2, 4}, N 1 = Q = 2, P = N t and QPSK modulation where codebooks are created by p.157 and p.159 in [5] for N 2 = 2 and N 2 = 4, respectively to evaluate (A) Selection of the DL factor (B) SINR performance and (C) Comparison with conventional works as following simulations.…”

PCQNet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems

“…Since the bottleneck of hardware cost and power consumption in the millimeter-wave mmWave Massive MIMO system will not appear in the uplink MU-MIMO scenarios, we adopt the digital precoding for its excellent performance in terms of sum rates. Some early works adopt the non-iterative [ 6 , 7 ] and iterative methods [ 8 , 9 ] to solve the highly non-convex problem of the joint transceiver optimization. The non-iterative precoding schemes are based on matrix decomposition, such as the singular value decomposition [ 6 ] and the QR decomposition [ 7 ], which cannot cope with the mismatch between the numbers of transmitting streams and the antennas.…”

“…Some early works adopt the non-iterative [ 6 , 7 ] and iterative methods [ 8 , 9 ] to solve the highly non-convex problem of the joint transceiver optimization. The non-iterative precoding schemes are based on matrix decomposition, such as the singular value decomposition [ 6 ] and the QR decomposition [ 7 ], which cannot cope with the mismatch between the numbers of transmitting streams and the antennas. We note that the centralized iterative precoding scheme utilizing the method of Lagrange multipliers can solve the mismatch between the numbers of transmitting streams and the antennas.…”

Joint Automatic Gain Control and Receiver Design for Quantized Large-Scale Mu-Mimo Systems

“…In this thesis the imperfect CSIR is based on the additive error model of the channel gain proposed in [65][66][67][68][69], were the channel estimation error that perturb the channel gain is considered to be a zero-mean circularly symmetric complex Gaussian variable with e ∼ CN (0, σ 2 e ). Here, σ 2 e indicates the estimation-error variance.…”