Dynamic resource allocation and energy efficiency optimization for a UAV-assisted ISAC system based on deep reinforcement learning

Abstract

UAVs are key for ISAC due to their mobility and coverage, but existing resource allocation methods lack adaptability in dynamic environments. This paper proposes a dynamic resource allocation framework for UAV-assisted ISAC using the SAC deep reinforcement learning algorithm. The framework constructs a state space with UAV parameters and jointly optimizes power control, channel selection, and task scheduling in a continuous action space. A maximum entropy mechanism balances exploration and energy-efficient decision-making. Simulations show the SAC-based approach outperforms benchmarks in task completion rate, energy consumption, and convergence. Specifically, it achieves 93.7% task completion, 36.9 J energy consumption, and 3.96 bit/J energy efficiency, with a 24.5% gain over DDPG. It converges within 310 seconds, faster than TD3 and PPO. These results validate the maximum entropy mechanism's effectiveness in enhancing exploration and generalization in UAV-assisted ISAC environments.