Investigating Sparse Reconfigurable Intelligent Surfaces (SRIS) via Maximum Power Transfer Efficiency Method Based on Convex Relaxation

TL;DR

This paper explores the design of sparse reconfigurable intelligent surfaces (SRIS) using convex relaxation to optimize power transfer efficiency, demonstrating that fewer tunable components can still achieve substantial performance in wireless systems.

Contribution

It adapts a semidefinite relaxation optimization method to evaluate SRIS configurations, enabling reliable performance analysis with minimal reconfigurable elements.

Findings

Using only 50% of reconfigurable elements still yields significant performance gains.

The relaxation method provides tight bounds, ensuring reliable maximum performance evaluation.

Sparse RIS configurations can effectively balance efficiency and system complexity.

Abstract

Reconfigurable intelligent surfaces (RISs) are widely considered to become an integral part of future wireless communication systems. Various methodologies exist to design such surfaces; however, most consider or require a very large number of tunable components. This not only raises system complexity, but also significantly increases power consumption. Sparse RISs (SRISs) consider using a smaller or even minimal number of tunable components to improve overall efficiency while maintaining sufficient RIS capability. The versatile semidefinite relaxation-based optimization method previously applied to transmit array antennas is adapted and applied accordingly, to evaluate the potential of different SRIS configurations. Because the relaxation is tight in all cases, the maximum possible performance is found reliably. Hence, with this approach, the trade-off between performance and sparseness of SRIS can be analyzed. Preliminary results show that even a much smaller number of reconfigurable elements, e.g. only 50%, can still have a significant impact.