Development of Multi-agent Deep Reinforcement Learning with Prioritized Experience Replay for Communication and Sensing Integrated Network in 5G mmWave System

Abstract

The ability of various isolated devices to sense their surroundings can be improved by 5G millimetre wave (mmWave) communication technology. By jointly supporting data transmission and sensing tasks, the framework improves overall spectrum efficiency in wireless networks. Among them, the Integrated Sensing and Communication (ISAC) has become the standard in wireless communications. Specifically, mmWave technology is highly effective for bandwidth-intensive communication services and delivers improved spatial and temporal accuracy through its large spectrum availability and directional beamforming characteristics. To meet the requirements, a multi-agent-based deep learning technique is proposed for better development. Over this sensing network of 5G mmWave, the resource allocation process is handled by Multi-agent Deep Reinforcement Learning with Prioritized Experience Replay (MDRL-PER), whereas the system is provided based on allocated resource for better communication. Finally, the performance of the system is assessed through distinct evaluation metrics and compared with existing methodologies. Hence, the superior results are obtained to ensure the efficacy of the communication network.