In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where the RIS is modeled using multiport network theory based on theZ-parameter representation. Unlike conventional RIS models based on reflection-coefficient matrices, we characterize RIS reconfigurability with tunable circuit impedances, thus capturing the electromagnetic mutual coupling (MC) effects among RIS elements. Specifically, we jointly optimize the transmit covariance matrix at the base station (BS) and the RIS tunable load impedance matrix to maximize the radar signal-to-noise ratio (SNR). The power budget at the BS and the quality of service (QoS) constraints for the communication users are also satisfied. To highlight the impact of electromagnetic MC on the system, both the no-MC and the MC-aware cases are considered. For the non-convex MC-aware problem, we propose an alternating optimization (AO) algorithm that integrates Neumann series approximation, semidefinite relaxation (SDR), and sequential rank-one constraint relaxation (SROCR) techniques. As a simplified form of the MC-aware case, the no-MC case can be considered as a sub-algorithm embedded in the proposed solution framework. Numerical results show that electromagnetic MC significantly affects the system performance, especially under sub-wavelength spacing.

In this paper, we investigate an integrated sensing and communication (ISAC) system assisted by a dynamic metasurface antenna (DMA), where the base station (BS) simultaneously communicates with multiple users and performs target sensing. Specifically, this paper aims to maximize the radar signal-to-noise ratio (SNR) at the BS by jointly optimizing the beamforming matrix and the DMA weight matrix, subject to signal-to-interference-plus-noise ratio (SINR) constraints for the users, the maximum transmit power at the BS and the Lorentzian constraint associated with the DMA elements. To tackle this non-convex optimization problem, an alternating optimization (AO) algorithm is proposed. In this algorithm, semidefinite relaxation (SDR) is employed to optimize the beamforming matrix, while sequential rank-one constraint relaxation (SRCR) is used to optimize the DMA weight matrix. Additionally, the penalty dual decomposition (PDD) and successive convex approximation (SCA) techniques are utilized as an alternative approach to solve the DMA weight matrix. Simulation results demonstrate that the DMA-assisted ISAC system achieves favorable results. The impact of different parameters on the objective value is analyzed, which shows that the PDD method outperforms the SRCR method.

This paper proposes a three-stage uplink channel estimation protocol for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems, where both the base station (BS) and the RIS are equipped with uniform planar arrays (UPAs). The proposed approach explicitly accounts for the mutual coupling (MC) effect, modeled via scattering parameter multiport network theory. In Stage~I, a dimension-reduced subspace-based method is proposed to estimate the common angle of arrival (AoA) at the BS using the received signals across all users. In Stage~II, MC-aware cascaded channel estimation is performed for a typical user. The equivalent measurement vectors for each cascaded path are extracted and the reference column is reconstructed using a compressed sensing (CS)-based approach. By leveraging the structure of the cascaded channel, the reference column is rearranged to estimate the AoA at the RIS, thereby reducing the computational complexity associated with estimating other columns. Additionally, the common angle of departure (AoD) at the RIS is also obtained in this stage, which significantly reduces the pilot overhead for estimating the cascaded channels of other users in Stage~III. The RIS phase shift training matrix is designed to optimize performance in the presence of MC and outperforms random phase scheme. Simulation results validate that the proposed method yields better performance than the MC-unaware and existing approaches in terms of estimation accuracy and pilot efficiency.