Joint Task Offloading and Resource Allocation in RIS-Assisted NOMA-VEC Intent-Based Networking

In intent-based vehicular edge computing (VEC) networking, escalating demands for computational offloading and resource management in dynamic urban environments necessitate innovative solutions. This article proposes a novel reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA)-VEC framework that empowers vehicle users (VUs) to offload arbitrary task portions to multiple edge servers via any available subcarrier. This approach overcomes limitations posed by heterogeneous local computing capabilities and stringent latency constraints. By leveraging RISs to enhance channel conditions through both direct and reflected links, our framework significantly improves communication reliability and offloading efficiency. To minimize the average weighted energy consumption of VUs under time-varying channels and traffic dynamics, we formulate a joint optimization problem integrating offloading decisions, power allocation, and transmission time scheduling. Addressing the problem's inherent complexity, characterized by multivariable coupling and nonconvex constraints, we develop a two-stage decomposition strategy: Offloading decisions are dynamically adapted to environmental fluctuations using a proximal policy optimization (PPO)-based algorithm, while resource allocation is resolved through a hybrid genetic algorithm (GA) and sequential least squares programming (SLSQP) approach, efficiently navigating combinatorial and nonconvex landscapes. Extensive simulations demonstrate that our framework reduces VU energy consumption by 11.12% compared with baseline methods, validating its superior efficiency in RIS-enhanced VEC systems.