Computation Pre-Offloading via Trajectory Prediction for MEC-Enabled Vehicular Networks: Design Optimization and Analysis

Task offloading is crucial for the efficient management of vehicular wireless networks, requiring access to real-time information on network status and available computational resources. However, because vehicles are mobile network nodes with variable speeds and limited computational resources for near-real-time task offloading, task offloading schemes may incur high latency, rendering this goal practically infeasible. To address this issue, this paper presents a trajectory-prediction-based pre-offloading decision (TPPD) algorithm that analyzes historical vehicle trajectories to predict future coordinates, thereby enabling the allocation of computational resources in advance. We first use a Long Short-Term Memory (LSTM) network to predict vehicle trajectories with error correction. Then, based on the task requirements and predicted trajectories, a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) is designed to enable the multi-access edge server to minimize task processing delay while ensuring effective utilization of available computational resources. Numerical results verify the effectiveness of the proposed approach, demonstrating that, compared to traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization.