Work

Anti-Jamming Resource Allocation for Integrated Sensing and Communications Based on Game-Guided Reinforcement Learning

Year: 2024

Type: article

Abstract: Jamming attacks severely degrade both the sensing and communication performances, and thus this letter investigates the problem of anti-jamming resource allocation optimization in integrated sensing and communication (ISAC) systems. Our objective is to maximize the weighted sum of the communication rate and the effective sensing power while meeting both communication and sensing requirements against malicious jamming. Since the joint optimization of communication and sensing is a highly coupled problem as well as the jamming behavior is dynamic, we then propose an advanced game-guided deep reinforcement learning (DRL) algorithm to address the resource allocation issue. Specifically, the power control problem is modeled as a Markov Decision Process (MDP), while the channel selection problem is formulated as a Stackelberg game. We further prove the existence of a Stackelberg equilibrium (SE). Simulation results demonstrate that the proposed DRL-based-anti-jamming approach significantly enhances the communication and sensing performances of ISAC systems compared to other baseline methods, supporting superior resistance to inter-channel interference (ICI) and jamming attacks.

Source: IEEE Wireless Communications Letters

DOI: 10.1109/lwc.2024.3496437

Authors: Yihui Chen, Helin Yang, Xiaoyu Ou, Yifu Jiang, Zehui Xiong

Institutions: Xiamen University, University of Bristol, Universidad de Zaragoza, Singapore University of Technology and Design

Language: English

Topic: Radar Systems and Signal Processing

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green