Dynamic metasurface antennas (DMAs) have emerged as a promising solution for alleviating the high power consumption, hardware cost, and physical size of multiple-input multiple-output (MIMO) architectures. This paper investigates a DMA-enabled multicast system with finite-alphabet inputs, considering the availability of either instantaneous or statistical channel state information (CSI). For the instantaneous CSI scenario, a multicast rate (MR) maximization problem is formulated, and an efficient two-stage equivalent precoder approximation (EPA)–based algorithm is proposed to jointly optimize the digital precoder and the DMA weight matrix. For the statistical CSI scenario, the ergodic MR (EMR) maximization problem is considered. To gain fundamental insights into the system design, asymptotic analyses of the EMR are conducted in the high signal-to-noise ratio (SNR) regime. The results reveal that the EMR converges to a finite constant as the SNR approaches infinity, with the convergence rate (CR) determined by the diversity order and the array gain. Motivated by these analytical insights, an array gain maximization problem is formulated and an EPA-based algorithm is proposed. Numerical results demonstrate the effectiveness of the proposed algorithms, validate the correctness of the derived high-SNR EMR analysis, and confirm the effectiveness of using DMA to enhance multicast transmission.

The aim of this article is to investigate the performance of near-field integrated sensing and communication (ISAC) systems using rotatable movable antennas (RMAs). In the proposed RMA-enabled system, the positions and rotations of antennas at the base station (BS) are dynamically adjusted to enhance both communication and sensing capabilities. Two designs are explored: 1) a sensing-centric design that minimizes the Cramér–Rao bound (CRB) with signal-to-interference-plus-noise (SINR) ratio constraints and 2) a communication-centric design that maximizes the sum-rate with a CRB constraint. To solve the formulated optimization problems, two alternating optimization (AO)-based algorithms are proposed capitalizing on the semidefinite relaxation (SDR) method and the particle swarm optimization (PSO) method. Numerical results demonstrate that: 1) the proposed rotatable MA (RMA)-enabled system outperforms the conventional fixed-position antenna and nonrotatable movable antenna (MA) systems in both sensing-centric and communication-centric designs and RMAs’ rotations show a higher performance gain in communication-centric design and 2) the proposed optimization methods achieve the Pareto boundary in both sensing-centric and communication-centric designs.

Dynamic metasurface antennas (DMAs) offer the potential to achieve large-scale antenna arrays with low power consumption and reduced hardware costs, making them a promising technology for future communication systems. This paper investigates the spectral efficiency (SE) of DMA-enabled multiuser multiple-input single-output (MISO) systems in both uplink and downlink transmissions, using only statistical channel state information (CSI) to maximize the ergodic sum rate of multiple users. For the uplink system, we consider two decoding rules: minimum mean square error (MMSE) with and without successive interference cancellation (SIC). For both decoders, we derive closed-form surrogates to substitute the original expressions of ergodic sum rate and formulate tractable optimization problems for designing DMA weights. Then, a weighted MMSE (WMMSE)-based algorithm is proposed to maximize the ergodic sum rate. For the downlink system, we derive an approximate expression for the ergodic sum rate and formulate a hybrid analog/digital beamforming optimization problem that jointly optimizes the digital precoder and DMA weights. A penalty dual decomposition (PDD)-based algorithm is proposed by leveraging the fractional programming framework. Numerical results validate the accuracy of the derived surrogates and highlight the superiority of the proposed algorithms over baseline schemes. It is shown that these algorithms are effective across various DMA settings and are particularly well-suited for system design in fast time-varying channels.

This paper analyzes the ergodic mutual information (EMI) of an intelligent reflecting surface (IRS)-aided multiple-input single-output system with finite-alphabet inputs relying on two- timescale channel state information. For the sake of unveiling important system design insights, asymptotic analyses are performed on the EMI in the regime of high signal-to-noise ratio (SNR). It is found that the EMI converges to some constant in the high-SNR regime and the rate of convergence is determined by the diversity order and the array gain. On this basis, two efficient algorithms are proposed to design the phase shifts of the IRS in order to improve the array gain as well as the EMI. All the analytical results are verified through computer simulations.

Antenna selection can handle the cost and complexity issues in massive multiple-input multiple-output (MIMO) channels. In this article, the sum-rate capacity of a multiuser massive MIMO uplink channel is analyzed. A mathematically tractable sum-rate capacity upper bound is derived for the considered system. Furthermore, for a sufficiently large base station (BS) antenna number, a deterministic equivalent (DE) of the sum-rate capacity bound is derived. Based on this DE, the sum-rate capacity is shown to grow double logarithmically with the number of BS antennas. Numerical experiments confirm the validity of the analytical results.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technique to enhance the system spectral efficiency. This paper investigates the ergodic channel capacity (ECC) of an RIS-aided multiple-input multiple-output channel under the assumption that the transmitter-RIS, RIS-receiver, and transmitter-receiver channels contain deterministic line-of-sight paths. Novel expressions are derived to characterize the upper and lower bounds of the ECC. To unveil more system insights, asymptotic analyses are performed to the system ECC in the limit of large signal-to-noise ratio (SNR) and number of reflecting elements (REs). Theoretical analyses suggest that the RIS’s deployment can shape the ECC curve by influencing its high-SNR power offset and the ECC can get improved by increasing the number of REs.