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.

This paper investigates a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MIMO) system by considering only the statistical channel state information (CSI) at the base station (BS). We aim to maximize its sum-rate via the joint optimization of precoding matrix at the BS and phase shifts vector at the RIS. However, the multi-user MIMO transmissions and the spatial correlations make the optimization cumbersome. For tractability, an asymptotic sum-rate is derived under a large number of the reflecting elements. By adopting the asymptotic sum-rate as the objective function, optimal designs of the transmit precoding matrix and the phase shifts vector can be decoupled and solved individually. More specifically, a high-quality suboptimal solution of the transmit precoding matrix and phase shifts vectors can be obtained by utilizing the water-filling algorithm and the projected gradient ascent (PGA) algorithm, respectively. Comparing to the case of the instantaneous CSI assumed at the BS, the proposed algorithm based on the statistical CSI can achieve comparable performance but with much lower channel estimation overhead, information feedback overhead, and computational complexity, which is more affordable and appealing for practical applications. Moreover, the impact of spatial correlation on the asymptotic sum-rate is examined by using majorization theory.

In this paper, a novel 3D cellular model consisting of aerial base stations (aBSs) and aerial user equipments (aUEs) is proposed, by integrating the coordinated multi-point (CoMP) transmission technique with the theory of stochastic geometry. For this new 3D architecture, a tractable model for aBSs’ deployment based on the binomial-Delaunay tetrahedralization is developed, which ensures seamless coverage for a given space. In addition, a versatile and practical frequency allocation scheme is designed to eliminate the inter-cell interference effectively. Based on this model, performance metrics including the achievable data rate and coverage probability are derived for two types of aUEs: i) the general aUE (i.e., an aUE having distinct distances from its serving aBSs) and ii) the worst-case aUE (i.e., an aUE having equal distances from its serving aBSs). Simulation and numerical results demonstrate that the proposed approach emphatically outperforms the conventional binomial-Voronoi tessellation without CoMP. Insightfully, it provides a similar performance to the binomial-Voronoi tessellation which utilizes the conventional CoMP scheme; yet, introducing a considerably reduced computational complexity and backhaul/signaling overhead.

This paper develops an interference aware design for cooperative hybrid automatic repeat request (HARQ) assisted non-orthogonal multiple access (NOMA) scheme for large-scale device-to-device (D2D) networks. Specifically, interference aware rate selection and power allocation are considered to maximize long term average throughput (LTAT) and area spectral efficiency (ASE). The design framework is based on stochastic geometry that jointly accounts for the spatial interference correlation at the NOMA receivers as well as the temporal interference correlation across HARQ transmissions. It is found that ignoring the effect of the aggregate interference, or overlooking the spatial and temporal correlation in interference, highly overestimates the NOMA performance and produces misleading design insights. An interference oblivious selection for the power and/or transmission rates leads to violating the network outage constraints. To this end, the results demonstrate the effectiveness of NOMA transmission and manifest the importance of the cooperative HARQ to combat the negative effect of the network aggregate interference. For instance, comparing to the non-cooperative HARQ assisted NOMA, the proposed scheme can yield an outage probability reduction by 21%. Furthermore, an interference aware optimal design that maximizes the LTAT given outage constraints leads to 17% throughput improvement over HARQ assisted orthogonal multiple access (OMA) scheme.

In this article, we present a hybrid channel estimation algorithm for uniform planar array (UPA)-assisted millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems by exploiting the benefits from both the compressed sensing (CS) and the sparse Bayesian learning (SBL). Compared with existing studies, the distribution characteristics and correlations between propagation paths in the elevation (e)- and azimuth (a)-angle domains are considered to enhance the estimation performance. Specifically, we first redefine the e-angles and the a-angles to simplify the system model. Then, a novel autoregressive (AR)-Gaussian channel prior is proposed to capture both the sparsity and the clustering properties of mmWave massive MIMO IoT channels. After that, we provide a channel approximation method to overcome the channel uncertainty by exploiting the structure of the AR-Gaussian channel prior. The hybrid beamforming (HBF) architecture with limited radio-frequency (RF) chains in mmWave IoT systems is also considered. Finally, we propose a hybrid channel estimation algorithm, which consists of two stages. Based on the different distribution characteristics in different angle domains, the CS-based channel estimation is performed for e-angles on stage one, while the SBL-based channel estimation is applied for a-angles on stage two. Numerical results reveal that compared with the existing CS- and SBL-only methods, the proposed hybrid channel estimation algorithm exhibits better performance in terms of computational complexity, sparsity robustness, and estimation accuracy.