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.

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 improve the equivalent channel power gains and the system sum-capacity.

Spatial multiplexing (SM) offers a linear increase in transmission rate without bandwidth expansion or power increase. In SM systems, the LMMSE receiver establishes a good tradeoff between the complexity and performance. The performance of the LMMSE receiver would be degraded by MIMO channel estimation errors. This letter focus on obtaining the asymptotic convergence of output interference power and SIR performance for the LMMSE receiver with channel uncertainty. Exactly matched simulation results verify the validity of analysis in the large-system assumption. Furthermore, we find that the analytical results are also valid in the sense of average results for limited-scale system in spite of the asymptotic assumption used in derivation.

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.

Cognitive radio technology, which allows secondary user (SU) to utilize the spectrum holes left by primary user (PU), was proposed to solve spectrum under-utilization problem. However, due to sensing error, SU's transmission will bring negative effects to PU's communication. Recently, cooperative relay technology was introduced to solve this problem. In this paper, a cooperative framework, which allows SU to act as a relay for primary link when needed, is considered and then a cognitive relay scheme is proposed. In order to maximize SU's throughput while keeping the system stable, we study and obtain SU's optimal strategy (i.e., relaying strategy and power allocation) by a constrained optimization problem. Since energy consumption is also an important problem for cognitive radio networks, we also investigate SU's optimal strategy to maximize SU's energy efficiency while keeping the system stable. The numerical results show that the cognitive relay scheme can achieve higher throughput and energy efficiency than reference schemes.

In this letter, coordinated power allocation (PA) is investigated for the downlink of a generalized multi-cluster distributed antenna system (DAS). Motivated by practical applications, we assume only the global large-scale channel state information is known at the transmitter. First, an upper bound (UB) for the ergodic sum capacity of the system is derived and used as a simplified optimization target. Then, a coordinated PA scheme is proposed based on Geometric Programming (GP), which is demonstrated to be nearly optimal by Monte Carlo simulations.

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.

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.

In this letter, we address the problem of downlink power allocation for the generalized distributed antenna system (DAS) with cooperative clusters. Considering practical applications, we assume that only the large-scale channel state information is available at the transmitter. The power allocation scheme is investigated with the target of ergodic achievable sum rate maximization. Based on some approximations and the Rayleigh Quotient Theory, the simple selective power allocation scheme is derived for the low SNR scenario and the high SNR scenario, respectively. The methods are applicable in practice due to their low complexity.

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 sum-capacity, 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 sum-capacity can be improved effectively.

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.

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.