IRS-assisted Edge Computing for Vehicular Networks: A Generative Diffusion Model-based Stackelberg Game Approach

TL;DR

This paper introduces a novel IRS-assisted MEC framework for vehicular networks, employing a generative diffusion model within a Stackelberg game to optimize task delay and energy consumption under complex, dynamic conditions.

Contribution

It proposes a new GDM-based Stackelberg game approach to solve the challenging joint optimization problem in IRS-assisted vehicular MEC networks.

Findings

GDMSG outperforms benchmark methods in simulations.

The approach effectively balances delay and energy consumption.

The model captures complex network dynamics efficiently.

Abstract

Recent advancements in intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) offer new opportunities to enhance the performance of vehicular networks. However, meeting the computation-intensive and latency-sensitive demands of vehicles remains challenging due to the energy constraints and dynamic environments. To address this issue, we study an IRS-assisted MEC architecture for vehicular networks. We formulate a multi-objective optimization problem aimed at minimizing the total task completion delay and total energy consumption by jointly optimizing task offloading, IRS phase shift vector, and computation resource allocation. Given the mixed-integer nonlinear programming (MINLP) and NP-hard nature of the problem, we propose a generative diffusion model (GDM)-based Stackelberg game (GDMSG) approach. Specifically, the problem is reformulated within a Stackelberg game framework, where generative GDM is integrated to capture complex dynamics to efficiently derive optimal solutions. Simulation results indicate that the proposed GDMSG achieves outstanding performance compared to the benchmark approaches.