Dynamic Beamforming and Power Allocation in ISAC via Deep Reinforcement Learning

TL;DR

This paper introduces a DRL-based method for real-time beamforming and power allocation in ISAC systems, significantly improving runtime efficiency and spectral efficiency compared to traditional optimization benchmarks.

Contribution

The paper presents a novel DRL approach for dynamic resource allocation in ISAC, demonstrating superior runtime and spectral efficiency over existing methods.

Findings

DRL converges within 2000 episodes.

Achieves up to 80% of SDR benchmark spectral efficiency.

Decision times reduced to around 20 ms.

Abstract

Integrated Sensing and Communication (ISAC) is a key enabler in 6G networks, where sensing and communication capabilities are designed to complement and enhance each other. One of the main challenges in ISAC lies in resource allocation, which becomes computationally demanding in dynamic environments requiring real-time adaptation. In this paper, we propose a Deep Reinforcement Learning (DRL)-based approach for dynamic beamforming and power allocation in ISAC systems. The DRL agent interacts with the environment and learns optimal strategies through trial and error, guided by predefined rewards. Simulation results show that the DRL-based solution converges within 2000 episodes and achieves up to 80\% of the spectral efficiency of a semidefinite relaxation (SDR) benchmark. More importantly, it offers a significant improvement in runtime performance, achieving decision times of around 20 ms compared to 4500 ms for the SDR method. Furthermore, compared with a Deep Q-Network (DQN) benchmark employing discrete beamforming, the proposed approach achieves approximately 30\% higher sum-rate with comparable runtime. These results highlight the potential of DRL for enabling real-time, high-performance ISAC in dynamic scenarios.