IRS-Enhanced V2X Communication and Computation Systems: Resource Allocation and Performance Optimization
Xueyan Cao, Shubin Wang, Xiaolei Ren
Abstract
Vehicle-to-Everything (V2X) communication and computation encounter challenges in achieving ultrareliable, low-latency communication, and optimizing energy consumption in dynamic vehicular environments. To overcome these issues, intelligent reflecting surfaces (IRSs) are introduced to boost communication efficiency and reliability while lowering latency and energy use. This article presents an IRS-enhanced V2X system, employing multiple IRSs to improve vehicle communication and computation offloading through spectrum reuse principles. An effective utility function is developed to quantify total latency and energy consumption, facilitating precise system evaluation and optimization. The complex optimization problem is divided into four subproblems: 1) vehicle operation mode selection; 2) spectrum reuse allocation; 3) computation offloading decision; and 4) beamforming design. A swap-matching-based tabu-search method solves the mode selection subproblem, while semi-definite relaxation and penalty functions address the other issues integrated through an alternating optimization algorithm. The optimized system achieves efficient and reliable communication with reduced latency and energy consumption. Simulations reveal that effective utility function minimization significantly enhances system efficiency compared to benchmarks. Strategic IRS deployment reduces channel losses, resulting in substantial performance improvements and supporting intelligent, sustainable transportation network advancement.