On-Demand Environment Perception and Resource Allocation for Task Offloading in Vehicular Networks

In vehicular edge computing networks, the real-time, on-demand scheduling of scarce network resources for environmental perception, task offloading, computation, and feedback is vital. However, these coupled processes make resource allocation challenging. Moreover, existing real-time channel measurement techniques in complex vehicular topologies present load, accuracy, and customization difficulties. To address these issues, this paper proposes an on-demand environmental perception and resource allocation strategy. Specifically, with the introduction of a channel knowledge base, we first analyze the coupling relationship between environmental perception, communication, and computation. A model is then proposed for task offloading to schedule the granularity of environment perception, communication resources, and computational resources dynamically. Subsequently, the resource allocation problem is formulated as an optimization problem, aiming to minimize system processing delay and maximize resource utilization while ensuring perception accuracy. To address this, a two-phase optimization-assisted deep reinforcement learning (DRL) algorithm is proposed. The initial phase uses convex optimization to approximate a solution. The second phase proposes a DRL-based algorithm to intelligently schedule dynamic network resources, with the first phase’s solution guiding the initial exploration space to enhance DRL training efficiency. Extensive simulation experiments verify the effectiveness of our proposal.