DRL-Driven Dual-Stage Resource Optimization Strategy for Efficient Computational Offloading in MEC-Enabled Vehicular Networks

In the contemporary landscape of vehicular networks, characterized by disruptive innovations such as autonomous driving, real-time traffic monitoring, and integrated infotainment services, there is a greater need for substantial computational power and precise operations. To cope with such demand, mobile edge computing (MEC) emerges as a pivotal mechanism that can boost the capacities of vehicular networks, leveraging edge servers, offering ultralow latency, and providing support for operations requiring near real-time access to rapidly growing and varying data. In this paper, we exploit deep reinforcement learning (DRL) techniques to minimize the overall service delay of the vehicle in a vehicular network using MEC. We propose a dual-stage DRL-assisted resource optimization strategy; firstly, the strategy optimizes the transmit power of the vehicle using the deep deterministic policy gradient (DDPG) algorithm after the vehicle initiates computational offloading, which contributes to minimizing the propagation delay. Secondly, once the data are transferred to the roadside unit (RSU), the strategy uses the deep Q-network (DQN) algorithm to allocate an optimal number of processing cores for computational tasks, thus minimizing the computational delay. Additionally, we present the energy efficiency (EE) and spectral efficiency (SE) analysis of the vehicle to examine the trade-off between the EE and SE. We compare the performance of the proposed strategy against the benchmark schemes, including always offload, never offload, random offload, and deep neural network (DNN)–based cooperative offloading schemes. The simulation results demonstrate that the proposed resource allocation strategy outperforms the benchmark schemes and improves the system performance by reducing the overall service delay.