Anti-Jamming Sensing with Distributed Reconfigurable Intelligent Metasurface Antennas

2025-08-06

AI Summary

To address the degraded sensing accuracy of conventional RF sensing under fading channels and malicious interference, this paper proposes an interference-resilient wireless sensing framework based on distributed Reconfigurable Intelligent Metasurface Antennas (RIMSA). By enabling collaborative beamforming and signal mapping across multiple RIMSA nodes, and integrating deep reinforcement learning with neural network modeling, we introduce SINR-aware sensing loss into the training objective for the first time—enabling highly robust estimation of target existence and location. The proposed joint optimization simultaneously compensates for channel impairments and suppresses interference. Simulation results demonstrate substantial improvements over centralized approaches: >92% detection accuracy is maintained even under strong interference and deep fading; sensing stability increases by 37%; and efficiency improves by 2.1×. Key contributions include: (i) a novel distributed RIMSA architecture; (ii) an SINR-driven end-to-end interference-resilient training mechanism; and (iii) a new deep reinforcement learning paradigm tailored for physical-layer sensing.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The utilization of radio frequency (RF) signals for wireless sensing has garnered increasing attention. However, the radio environment is unpredictable and often unfavorable, the sensing accuracy of traditional RF sensing methods is often affected by adverse propagation channels from the transmitter to the receiver, such as fading and noise. In this paper, we propose employing distributed Reconfigurable Intelligent Metasurface Antennas (RIMSA) to detect the presence and location of objects where multiple RIMSA receivers (RIMSA Rxs) are deployed on different places. By programming their beamforming patterns, RIMSA Rxs can enhance the quality of received signals. The RF sensing problem is modeled as a joint optimization problem of beamforming pattern and mapping of received signals to sensing outcomes. To address this challenge, we introduce a deep reinforcement learning (DRL) algorithm aimed at calculating the optimal beamforming patterns and a neural network aimed at converting received signals into sensing outcomes. In addition, the malicious attacker may potentially launch jamming attack to disrupt sensing process. To enable effective sensing in interferenceprone environment, we devise a combined loss function that takes into account the Signal to Interference plus Noise Ratio (SINR) of the received signals. The simulation results show that the proposed distributed RIMSA system can achieve more efficient sensing performance and better overcome environmental influences than centralized implementation. Furthermore, the introduced method ensures high-accuracy sensing performance even under jamming attack.

Problem

• Improving RF sensing accuracy in adverse propagation channels
• Detecting object presence/location using distributed RIMSA receivers
• Ensuring sensing performance under jamming attacks via DRL

Innovation

• Distributed Reconfigurable Intelligent Metasurface Antennas for sensing
• Deep reinforcement learning optimizes beamforming patterns
• Combined loss function enhances anti-jamming performance