Improved TD3 Based Resource Allocation Optimization for Latency-sensitive Tasks in ISAC-aided VEC

Vehicular edge computing (VEC) has emerged to address the increasing demands on wireless networks posed by massive data and diverse applications in intelligent vehicular services. However, challenges such as low spectrum utilization due to massive sensor deployment, signal degradation from high-speed mobility, and computational resource allocation issues hinder the real-time and secure operation of intelligent vehicles. Therefore, we propose a resource allocation optimization method for VEC based on Integrated Sensing and Communication (ISAC) and Orthogonal Time Frequency Space (OTFS) technologies. Specifically, OTFS is leveraged to multiplex roadside unit (RSU) radar resources, improving spectrum efficiency. We develop comprehensive models for communication, vehicle mobility, sensing, delay, energy consumption, and formulate a delay-minimization resource allocation problem. The problem is modeled as a Markov Decision Process and solved with an improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which incorporates prioritized experience sampling and dynamic parameter update to accelerate training and enhance agent-environment interaction. Extensive simulations are conducted in a VEC environment, where the proposed algorithm is compared with DDQN, MADDPG, and MRL-DDPG. The results demonstrate that our method effectively mitigates the impact of vehicle mobility on signal transmission and significantly reduces task completion delay compared to existing algorithms.