混合智能反射面辅助感通算一体化车联网的联合功率时间分配方法  

Hbrid Roogroe lelient Sutaee Asiste Sasing to。AS Communication and Computation for Joint Power and Time Aloaiono in Vehicle Ad-hoc Network

作　　者：束锋 张钧豪[1] 张旗 姚誉 卞弘艺 王咸鹏 SHU Feng;ZHANG Junhao;ZHANG Qi;YAO Yu;BIAN Hongyi;WANG Xianpeng(School of Information and Communication Engineering,Hainan Universily,Haikou 570228,China;School of Electronic and Optical Engineering,Nanjing Unitersity of Science and Technology,Nanjing 210094,China)

机构地区：[1]海南大学信息与通信工程学院,海南570228 [2]南京理工大学电子工程与光电技术学院,南京210094

出　　处：《电子与信息学报》2025年第4期1026-1042,共17页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(U22A2002,62071234);海南省科技专项基金(ZDKJ2021022);海南大学科研启动项目(KYQD(ZR)-21008);海南大学信息技术协同创新中心项目(XTCX2022XXC07)。

摘　　要：当前车联网(V2X)环境普遍存在频谱资源紧缺和数据传输效率低的问题。该文通过集成感知、通信和计算车联网系统(ISCC-V2X)以提升车辆用户的数据传输能力。ISCC-V2X采用雷达感知技术帮助次用户接入主用户频谱空洞进行车联网通信,在车辆用户加入计算单元提升数据传输卸载能力,为了史好地捉升车联网通信和计算性能并同时降低系统功耗,在ISCC-V2X中引入混合智能反射面(H-RIS)。该研究从时间和功率资源分配的介度出发,对H-RIS辅叻的TSCC-V2X技术迹行了深入探讨。该文采用了一种两阶段的优化方法,对功率分配、时间分配和反射元件迹行交替优化求解,以找到最佳的优化方案,并通过定义联合吞吐量(JTC)的性能指标来丧征次用户的数掂传输能力和计算性能。通过仿真实验分析表明,在H-RIS辅助ISCC-V2X场景存在一种时间功率联合分配的最优策略,能够显著提升次用的联合吞吐虽。Objective Vehicular networks,as key components of intelligent transportation systems,are encountering increasing spectrum resource limitations within their dedicated 25 MHz communication band,as well as challenges from electromagnetic interference in typical communication environments.To address these issues,this paper integrates cognitive radio technology with radar sensing and introduces Hybrid-Reconfigurable Intelligent Surface(H-RIS)to jointly optimize radar sensing,data transmission,and computation.This approach aims to enhance spectrum resource utilization and the Joint Throughput Capacity(JTC)of vehicular networks.Methods A phased optimization approach is adopted to alternately optimize power allocation,time allocation,and reflection components in order to identify the best solution.The data transmission capacity of secondary users is characterized by defining a performance index for JTP.The problem is tackled through a two-stage optimization strategy where power allocation,time allocation,and reflection element optimization are solved iteratively to achieve the optimal solution.First,a joint optimization problem for sensing,communication,and computation is formulated.By jointly optimizing time allocation,H-RIS reflection element coefficients,and power allocation,the goal is to maximize the joint throughput capacity.The block coordinate descent method decomposes the optimization problem into three sub-problems.In the optimization of reflection element coefficients,a stepwise approach is employed,where passive reflection elements are fixed to optimize active reflection elements and vice versa.Results and Discussions The relationship between joint throughput and the number of iterations for the proposed Alternating Optimization Iterative Algorithm(AOIA)is shown(Figure 4).The results indicate that both algorithms converge after a finite number of iterations.The correlation between the target secondary user's joint throughput and radar power is presented(Figure 5).In the H-RIS-assisted Integrated Sensing Commu

关 键 词：车联网 感知通信计算一体化 混合智能反射面 联合吞吐虽 资源分配 

分 类 号：T192[一般工业技术]