Chaotic Grey Wolf Optimization-based resource allocation for Vehicle-to-Everything communications,International Journal of Communication Systems

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Chaotic Grey Wolf Optimization-based resource allocation for Vehicle-to-Everything communications
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2021-06-27 , DOI: 10.1002/dac.4908
Ibtissem Brahmi _{1,

2} , Monia Hamdi ₃ , Faouzi Zarai ₁

Affiliation

Device-to-device (D2D) enables direct communication between Vehicle-to-Everything (V2X) devices. For V2X communications, efficient power control and radio sub-channel allocation schemes are expected to accommodate the growing demand for data traffic of the increasing number of vehicular devices. We propose to investigate the resource allocation problem for V2X networks. We categorize vehicle user equipment (VUEs) into safety and non-safety VUEs and cellular user equipment (CUEs) into real-time and non-real-time CUEs according to their communication types. The objective of the proposed model is to maximize the total throughput of the system while maintaining quality of service (QoS) for CUEs and VUEs. Chaotic Grey Wolf Optimization (CGWO) algorithm, a meta-heuristic and evolutionary algorithm, is used to solve the resource allocation problem in V2X communications. Finally, comparisons between the proposed scheme and two other meta-heuristic algorithms, namely, Particle Swarm Optimization (PSO) and GWO for resource allocation, are carried out. The simulation results prove the effectiveness and performance of the proposed scheme in terms of resource allocation for V2X. They also demonstrate that CGWO-based resource allocation scheme outperforms both PSO-based and GWO-based resource allocation schemes in terms of total achieved throughput.

中文翻译：

基于混沌灰狼优化的车对万物通信资源分配

设备到设备 (D2D) 支持车辆到一切 (V2X) 设备之间的直接通信。对于 V2X 通信，有效的功率控制和无线电子信道分配方案有望满足越来越多的车载设备对数据流量不断增长的需求。我们建议研究 V2X 网络的资源分配问题。我们根据通信类型将车辆用户设备 (VUE) 分为安全和非安全 VUE，将蜂窝用户设备 (CUE) 分为实时和非实时 CUE。所提出模型的目标是最大化系统的总吞吐量，同时保持 CUE 和 VUE 的服务质量 (QoS)。混沌灰狼优化（CGWO）算法，一种元启发式和进化算法，用于解决V2X通信中的资源分配问题。最后，将所提出的方案与其他两种元启发式算法，即用于资源分配的粒子群优化 (PSO) 和 GWO 进行比较。仿真结果证明了所提出方案在 V2X 资源分配方面的有效性和性能。他们还证明，就实现的总吞吐量而言，基于 CGWO 的资源分配方案优于基于 PSO 和基于 GWO 的资源分配方案。仿真结果证明了所提出方案在 V2X 资源分配方面的有效性和性能。他们还证明，就实现的总吞吐量而言，基于 CGWO 的资源分配方案优于基于 PSO 和基于 GWO 的资源分配方案。仿真结果证明了所提出方案在 V2X 资源分配方面的有效性和性能。他们还证明，就实现的总吞吐量而言，基于 CGWO 的资源分配方案优于基于 PSO 和基于 GWO 的资源分配方案。

更新日期：2021-08-04

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