Secrecy Energy Efficiency Maximization in RIS-Aided Networks: Active or Nearly-Passive RIS?

2025-07-09

AI Summary

This paper investigates the secrecy energy efficiency (SEE) maximization problem in reconfigurable intelligent surface (RIS)-assisted wireless networks, focusing on the fundamental trade-off between energy efficiency and physical-layer security for active versus near-passive RIS architectures. For both perfect and statistical channel state information (CSI) scenarios, we propose an iterative algorithm jointly optimizing user transmit power, RIS reflection coefficients, and base station receive filters. Theoretical analysis reveals that active RISs suffer from significantly degraded SEE due to high static power consumption, whereas near-passive RISs demonstrate superior green and secure communication potential under stringent power constraints. Numerical results validate the algorithm’s effectiveness and quantify the SEE crossover points between the two RIS types. The findings provide actionable design guidelines and theoretical foundations for deploying low-power, high-security RIS systems.

Technology Category

• Search and Optimization: Algorithm Configuration
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• Maximize secrecy energy efficiency in RIS-aided networks
• Compare active and nearly-passive RISs for SEE trade-offs
• Optimize transmit powers, RIS coefficients, and receive filters

Innovation

• Compares active and nearly-passive RISs for SEE
• Develops SEE maximization algorithms for optimization
• Analyzes trade-off between RIS types in SEE