Joint Task Offloading and Resource Allocation in RIS-assisted NOMA-VEC Intent-based Networking

Wang, Xiaotian, Meng Yi, Miaojiang Chen, et al. “Joint Task Offloading and Resource Allocation in RIS-Assisted NOMA-VEC Intent-Based Networking.” IEEE Internet of Things Journal, 2025, 1–1. https://doi.org/10.1109/JIOT.2025.3620606.

Rights

© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Subjects

Vehicle dynamics
Deep Reinforcement learning
Intent-based Networking
Resource management
Real-time systems
Servers
Heuristic algorithms
UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab)
Energy consumption
Non-orthogonal multiple access
Reconfigurable intelligent surfaces
Dynamic scheduling
Reconfigurable Intelligent Surface
NOMA

Abstract

In Intent-based Vehicular Edge Computing (VEC) networking, escalating demands for computational offloading and resource management in dynamic urban environments necessitate innovative solutions. This paper proposes a novel RIS-assisted 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 Reconfigurable Intelligent Surfaces (RIS) 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 multi-variable coupling and non-convex 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 non-convex landscapes. Extensive simulations demonstrate that our framework reduces VU energy consumption by 11.12% compared to baseline methods, validating its superior efficiency in RIS-enhanced VEC systems.