Joint Task Offloading and Resource Allocation for Intelligent Reflecting Surface-Aided Integrated Sensing and Communication Systems Using Deep Reinforcement Learning Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 24; p. 9896
• Main Authors: Yang, Liu, Wei, Yifei, Wang, Xiaojun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.12.2023; MDPI
• Subjects: Algorithms; Antennas; Artificial intelligence; Communication; Data mining; Data processing; Data transmission; Deep learning; deep reinforcement learning; Design; Efficiency; Energy consumption; integrated sensing and communication; intelligent reflecting surface; Machine learning; Markov processes; Optimization; Radar systems; resource allocation; integrated sensing and communication; intelligent reflecting surface; resource allocation; deep reinforcement learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23249896

This paper investigates an intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework to cope with the problem of spectrum scarcity and poor wireless environment. The main goal of the proposed framework in this work is to optimize the overall performance of the system, including sensing, communication, and computational offloading. We aim to achieve the trade-off between system performance and overhead by optimizing spectrum and computing resource allocation. On the one hand, the joint design of transmit beamforming and phase shift matrices can enhance the radar sensing quality and increase the communication data rate. On the other hand, task offloading and computation resource allocation optimize energy consumption and delay. Due to the coupled and high dimension optimization variables, the optimization problem is non-convex and NP-hard. Meanwhile, given the dynamic wireless channel condition, we formulate the optimization design as a Markov decision process. To tackle this complex optimization problem, we proposed two innovative deep reinforcement learning (DRL)-based schemes. Specifically, a deep deterministic policy gradient (DDPG) method is proposed to address the continuous high-dimensional action space, and the prioritized experience replay is adopted to speed up the convergence process. Then, a twin delayed DDPG algorithm is designed based on this DRL framework. Numerical results confirm the effectiveness of proposed schemes compared with the benchmark methods.