{"title":"基于深度强化学习的ris辅助V2V中继系统AoI最小化","authors":"Xiaolin Liang;Qianlong Liu;Wangbin Cao;Shuaiqi Liu;Xiongwen Zhao","doi":"10.1109/JIOT.2025.3564334","DOIUrl":null,"url":null,"abstract":"By leveraging the inherent ability of reconfigurable intelligence surface (RIS) to enhance wireless communication channels, the integration of RIS into simultaneous wireless information and power transfer (SWIPT)-enabled vehicle-to-vehicle (V2V) systems presents a promising solution to jointly enhance communication performance and energy harvesting efficiency. Building on this potential, an RIS-assisted V2V dual-hop relay system is constructed, which deploys RIS on the gate of relay vehicle user equipment (VUE), enabling efficient signal refraction from source to relay VUEs. To address the critical challenge of information freshness in such RIS-assisted systems, Age of Information (AoI) is adopted as the key metric. And the AoI optimization problem is formulated that jointly considers energy/data buffer capacity limitations, relay sustainability, and real-time packet freshness. To effectively resolve this optimization problem under dynamic vehicular conditions, a prioritized experience replay-dueling double deep Q network (PER-D3QN) scheme based on deep reinforcement learning (DRL) is proposed to make the optimal relay decision for AoI minimization. Numerical results demonstrate that the average AoI using the proposed PER-D3QN scheme is reduced by 20% compared with the existing schemes.","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"12 14","pages":"27450-27460"},"PeriodicalIF":8.9000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AoI Minimization for RIS-Assisted V2V Relay System With Deep Reinforcement Learning\",\"authors\":\"Xiaolin Liang;Qianlong Liu;Wangbin Cao;Shuaiqi Liu;Xiongwen Zhao\",\"doi\":\"10.1109/JIOT.2025.3564334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"By leveraging the inherent ability of reconfigurable intelligence surface (RIS) to enhance wireless communication channels, the integration of RIS into simultaneous wireless information and power transfer (SWIPT)-enabled vehicle-to-vehicle (V2V) systems presents a promising solution to jointly enhance communication performance and energy harvesting efficiency. Building on this potential, an RIS-assisted V2V dual-hop relay system is constructed, which deploys RIS on the gate of relay vehicle user equipment (VUE), enabling efficient signal refraction from source to relay VUEs. To address the critical challenge of information freshness in such RIS-assisted systems, Age of Information (AoI) is adopted as the key metric. And the AoI optimization problem is formulated that jointly considers energy/data buffer capacity limitations, relay sustainability, and real-time packet freshness. To effectively resolve this optimization problem under dynamic vehicular conditions, a prioritized experience replay-dueling double deep Q network (PER-D3QN) scheme based on deep reinforcement learning (DRL) is proposed to make the optimal relay decision for AoI minimization. Numerical results demonstrate that the average AoI using the proposed PER-D3QN scheme is reduced by 20% compared with the existing schemes.\",\"PeriodicalId\":54347,\"journal\":{\"name\":\"IEEE Internet of Things Journal\",\"volume\":\"12 14\",\"pages\":\"27450-27460\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Internet of Things Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10976738/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Internet of Things Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10976738/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
AoI Minimization for RIS-Assisted V2V Relay System With Deep Reinforcement Learning
By leveraging the inherent ability of reconfigurable intelligence surface (RIS) to enhance wireless communication channels, the integration of RIS into simultaneous wireless information and power transfer (SWIPT)-enabled vehicle-to-vehicle (V2V) systems presents a promising solution to jointly enhance communication performance and energy harvesting efficiency. Building on this potential, an RIS-assisted V2V dual-hop relay system is constructed, which deploys RIS on the gate of relay vehicle user equipment (VUE), enabling efficient signal refraction from source to relay VUEs. To address the critical challenge of information freshness in such RIS-assisted systems, Age of Information (AoI) is adopted as the key metric. And the AoI optimization problem is formulated that jointly considers energy/data buffer capacity limitations, relay sustainability, and real-time packet freshness. To effectively resolve this optimization problem under dynamic vehicular conditions, a prioritized experience replay-dueling double deep Q network (PER-D3QN) scheme based on deep reinforcement learning (DRL) is proposed to make the optimal relay decision for AoI minimization. Numerical results demonstrate that the average AoI using the proposed PER-D3QN scheme is reduced by 20% compared with the existing schemes.
期刊介绍:
The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.