This study considers a point-to-point wireless link, in which both the transmitter and receiver are equipped with multiple antennas. In addition, two nearly-passive reconfigurable metasurfaces are deployed, one in the immediate vicinity of the transmitter, and one in the immediate vicinity of the receiver. In this scenario, the system energy efficiency is optimized with respect to the transmit covariance matrix, and the reflection matrices of the two metasurfaces. A low-complexity algorithm is developed, which is converges to a first-order optimal point of the maximization problem.Moreover, closed-form expressions are derived for the metasurface matrices in the special case of single-antenna or single-stream transmission. A numerical performance analysis shows, in particular, that the considered architecture can provide significant energy efficiency gains compared to fully digital beamforming architectures.

This work addresses the problem of secrecy energy efficiency (SEE) maximization in RIS-aided wireless networks. The use of active and nearly-passive RISs are compared and their trade-off in terms of SEE is analyzed. Considering both perfect and statistical channel state information, two SEE maximization algorithms are developed to optimize the transmit powers of the mobile users, the RIS reflection coefficients, and the base station receive filters. Numerical results quantify the trade-off between active and nearly-passive RISs in terms of SEE, with active RISs yielding worse SEE values as the static power consumed by each reflecting element increases.

This work proposes a provably convergent and low complexity optimization algorithm for the maximization of the secrecy energy efficiency in the uplink of a wireless network aided by a reconfigurable intelligent surface (RIS), in the presence of an eavesdropper. The mobile users' transmit powers and the RIS reflection coefficients are optimized. Numerical results show the performance of the proposed methods and compare the use of active and nearly-passive RISs from an energy-efficient perspective.

This work addresses the topic of energy efficiency (EE) maximization in wireless networks aided by a reconfigurable intelligent surface (RIS), considering both active and nearly-passive RISs. Moreover, unlike available literature, the analysis considers that the RIS reflection constraints are not enforced separately on each reflecting element, but the RIS is characterized by a single reflection constraint that is jointly enforced on all the reflecting elements. This allows for a wider set of feasible RIS reflection matrices, which leads to better performance, which is theoretically analyzed. Then, two provably convergent energy efficiency maximization algorithms with polynomial complexity are developed, which optimize the reflection coefficients of the RIS, the mobile users’ transmit powers, and the linear filters at the base station. Numerical results are illustrated to quantify the performance of the proposed methods and identify the operating points in which active or nearly-passive RISs should be preferred from the energy efficiency standpoint.

This work addresses the issue of energy efficiency maximization in a multi-user network aided by a reconfigurable intelligent surface (RIS) with global reflection capabilities. Two optimization methods are proposed to optimize the mobile users’ powers, the RIS coefficients, and the linear receive filters. Both methods are provably convergent and require only the solution of convex optimization problems. The numerical results show that the proposed methods largely outperform heuristic resource allocation schemes.

This work addresses the comparison between active and passive RISs in wireless networks, with reference to the system energy efficiency (EE). Two provably convergent and computationally-friendly EE maximization algorithms are developed, which optimize the reflection coefficients of the RIS, the transmit powers, and the linear receive filters. Numerical results show the performance of the proposed methods and discuss the operating points in which active or passive RISs should be preferred from an energy-efficient perspective.