To harness the benefits of both reconfigurable intelligent surface (RIS) and dynamic metasurface antenna (DMA), we consider the hybrid RIS and DMA assisted multiple-input multiple-output (MIMO) multiple-access channel (MAC) over spatially correlated Rician fading, in which multiple multi-antenna users send the transmitted signals to the DMA-based base station (BS) with the assistance of a RIS. The objective is to maximize the achievable ergodic sum-rate by jointly designing the transmit covariance matrix of users, the phase shift matrix of RIS, and the DMA weight matrix at BS only with statistical channel state information. By capitalizing on large random matrix theory, a closed-form asymptotic ergodic sum-rate is first obtained. Then, we propose a modified water-filling algorithm to design the optimal transmit covariance matrix under the power consumption and specific absorption rate constraints. Next, we design the phase shift matrix of RIS via the projected gradient ascent algorithm, subject to the non-convex unit-modular constraint. To find the constrained DMA weight matrix, we further resort to the optimal solution of the unconstrained DMA problem and adopt the alternating optimization method. The proposed algorithm is numerically shown to improve the sum-rate compared to the baseline schemes, verifying the effectiveness of the proposed schemes.

Intelligent reflecting surface (IRS) is an emerging cost-efficient technology to enhance communication performance by implementing a large number of passive reflecting elements with tunable phases in wireless systems. In this paper, we propose a general framework for the IRS-aided MIMO system designs under both single-user and multi-user setups, in which the diverse performance metrics including weighted mutual information and weighted MSE, and the realistic multiple weighted power constraint are taken into consideration. Leveraging the alternating optimization approach, the optimal IRS phase shifts are obtained in semi-closed forms. Specifically, based on the matrix-monotonic optimization theory, it is found that optimizing IRS phase shifts is essentially equivalent to tuning the eigenvalues and the corresponding eigenvectors of the MSE matrix. Then the proposed general framework is extended to a multi-user system by introducing a majorization-minimization (MM)-based method for IRS phase shift optimization. Simulation results show that our proposed optimal design brings significant enhancement on the chosen performance metric compared to the traditional MIMO systems without the IRS, and also significantly outperforms various benchmark designs in both single-user and multi-user systems.

Reconfigurable intelligent surface (RIS) stands out as a promising technology by enhancing the electromagnetic wave propagation environment with its passive reflecting elements. In this paper, we focus on the ergodic sum-rate analysis and maximization of the RIS-assisted uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) under Rician fading by exploiting full statistical channel state information (CSI). The spatial correlations at the base station, the users, and the RIS are also considered. By using the replica method originated in statistical physics, the closed-form asymptotic ergodic sum-rate of the system is first derived in the large-system regime on the account of the unique channel structure of the RIS-assisted MIMO-MAC system. Then, based on the derived asymptotic ergodic sum-rate, we propose an alternating optimization (AO) algorithm to jointly design the transmit covariance matrix of users and the phase-shifting matrix of RIS with full statistical CSI. Simulation results are also demonstrated to verify the accuracy of the derived asymptotic ergodic sum-rate and the superiority of the proposed AO algorithm. The results reveal that the derived closed-form asymptotic ergodic sum-rate matches very well with the Monte Carlo results even for a small number of antennas, and the proposed AO algorithm can achieve up to 10 bps/Hz gain at high signal-to-noise (SNR) regime, which is therefore valuable for future RIS-assisted MIMO-MAC system designs.