Yunzhou Li

Limited Feedback Precoding Scheme for Downlink Multiuser MIMO Systems

In this letter, we propose a limited feedback precoding scheme based upon grassmannian beamforming and user selection for downlink multiuser MIMO systems. Conventional random beamforming scheme only enjoys significant performance gains with a large number of users, which limits its practical application. With proper codebook size the proposed scheme outperforms conventional random beamforming scheme when the number of users is small or moderate.

Per-Layer Optimization in Multiuser MIMO Systems with Tomlinson-Harashima Precoding

In this letter, we investigate the application of Tomlinson-Harashima precoding (THP) in the downlink of multiuser multiple-input multiple-output (MIMO) systems, where multiple antennas are located at all the transceivers. Based on the criterion of maximum system sumcapacity, a per-layer optimization scheme is proposed, in which the subchannel ordering and transceiver filters design are generated. In the proposed scheme, the successive character of THP can be fully exploited, so that both the minimum cost of interference suppression and the maximum power and diversity gains can be implemented, and hence, the system sumcapacity can be improved effectively.

Robust Transceiver Design for Multiuser MIMO Downlink with Channel Uncertainties

This letter addresses the problem of robust transceiver design for the multiuser multiple-input-multiple-output (MIMO) downlink where the channel state information at the base station (BS) is imperfect. A stochastic approach which minimizes the expectation of the total mean square error (MSE) of the downlink conditioned on the channel estimates under a total transmit power constraint is adopted. The iterative algorithm reported in [2] is improved to handle the proposed robust optimization problem. Simulation results show that our proposed robust scheme effectively reduces the performance loss due to channel uncertainties and outperforms existing methods, especially when the channel errors of the users are different.

Receive Antenna Selection for Multiuser MIMO Systems with Tomlinson-Harashima Precoding

SUMMARY In this letter, we discuss the problem of receive antenna selection in the downlink of multiuser multiple-input multiple-output (MIMO) systems with Tomlinson-Harashima precoding (THP), where the number of receivers is assumed equal to that of transmit antennas. Based on the criterion of maximum system sum-capacity, a per-layer receive antenna selection scheme is proposed. This scheme, which selects one receive antenna for each receiver, can well exploit the nonlinear and successive characteristics of THP. Two models are established for the proposed per-layer scheme and the conventional per-user scheme. Both the theoretical analysis and simulation results indicate that the proposed scheme can greatly

Low Complexity Scheduling Algorithms for Downlink Multiuser MIMO System

Downlink multiuser MIMO system has attracted considerable attention recently for its potential to increase the system capacity. However, due to the limitation on the number of transmit antennas, when there are more users than can be supported simultaneously in a cell, other multiple access schemes, such as TDMA, must be applied in combination with multiuser MIMO. In this paper, we aim to design practical user scheduling algorithms to maximize the system capacity. Because the brute-force search for optimal user allocation is computationally prohibitive, we propose three low complexity suboptimal scheduling algorithms that offer both low complexity and high performance.

Joint Receive Antenna Selection for Multi-User MIMO Systems with Vector Precoding

Vector precoding is a nonlinear broadcast precoding scheme in the downlink of multi-user MIMO systems which outperforms linear precoding and THP (Tomlinson-Harashima Precoding). This letter discusses the problem of joint receive antenna selection in the multi-user MIMO downlink with vector precoding. Based on random matrix analysis, we derive a simple heuristic selection criterion using singular value decomposition (SVD) and carry out an exhaustive search to determine for each user which receive antenna should be used. Simulation results reveal that receive antenna selection using our proposed criterion obtains the same diversity order as the optimal selection criterion.

Capacity Bound of MIMO Systems with MPSK Modulation and Time-Multiplexed Pilots

In this letter, we elucidate the ergodic capacity of multiple-input multiple-output (MIMO) systems with M-ary phase-shift keying (MPSK) modulation and time-multiplexed pilots in frequency-flat Rayleigh fading environment. With linear minimum mean square error (LMMSE) channel estimation, the optimal pilots design is presented. For mathematical tractability, we derive an easy-computing closed-form lower bound of the channel capacity. Based on the lower bound, the optimal power allocation between the data and pilots is also presented in closed-form, and the optimal training length is investigated by numerical optimization. It is shown that the transmit scheme with equal training and data power and optimized training length provides suboptimal performance, and the transmit scheme with optimized training length and training power is optimal. With the latter scheme, in most situations, the optimal training length equals the number of the transmit antennas and the corresponding optimal power allocation can be easily computed with the proposed formula.

Adaptive MMSE Algorithm Used in Turbo Iterative SoIC of V-Blast System

Based on the minimum mean square error (MMSE) detection with iterative soft interference cancellation (SoIC), we propose an adaptive MMSE (A-MMSE) algorithm which acts as an MMSE operator at the beginning of iteration and a maximum ratio combination (MRC) when the interference is nearly cancelled. In our algorithm, a modified metric matrix based on the reliability of soft information from the decoder output is multiplied by the interference part of channel correlation matrix to update the detection operator. The simulation results have shown that this A-MMSE iterative SoIC algorithm can achieve significant performance advantage over the traditional MMSE iterative SoIC algorithm.