{"title":"利用量子计算优化资源分配,共享独立 IRS","authors":"Takahiro Ohyama;Yuichi Kawamoto;Nei Kato","doi":"10.1109/TETC.2023.3292355","DOIUrl":null,"url":null,"abstract":"Intelligent reflecting surfaces (IRSs) have attracted attention as a technology that can considerably improve the energy utilization efficiency of sixth-generation (6G) mobile communication systems. IRSs enable control of propagation characteristics by adjusting the phase shift of each reflective element. However, designing the phase shift requires the acquisition of channel information for each reflective element, which is impractical from an overhead perspective. In addition, for multiple wireless network operators to share an IRS for communication, new infrastructure facilities and operational costs are required at each operator's end to control the IRS in a coordinated manner. Herein, we propose a wireless communication system using standalone IRSs to solve these problems. The standalone IRSs cover a wide area by periodically switching phase shifts, and each operator allocates radio resources according to their phase-shift switching. Furthermore, we derive a quadratic unconstrained binary optimization equation for the proposed system to optimize radio resource allocation using quantum computing. The results of computer simulations indicate that the proposed system and method can be used to achieve efficient communication in 6G mobile communication systems.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"11 4","pages":"950-961"},"PeriodicalIF":5.1000,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Resource Allocation Optimization by Quantum Computing for Shared Use of Standalone IRS\",\"authors\":\"Takahiro Ohyama;Yuichi Kawamoto;Nei Kato\",\"doi\":\"10.1109/TETC.2023.3292355\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Intelligent reflecting surfaces (IRSs) have attracted attention as a technology that can considerably improve the energy utilization efficiency of sixth-generation (6G) mobile communication systems. IRSs enable control of propagation characteristics by adjusting the phase shift of each reflective element. However, designing the phase shift requires the acquisition of channel information for each reflective element, which is impractical from an overhead perspective. In addition, for multiple wireless network operators to share an IRS for communication, new infrastructure facilities and operational costs are required at each operator's end to control the IRS in a coordinated manner. Herein, we propose a wireless communication system using standalone IRSs to solve these problems. The standalone IRSs cover a wide area by periodically switching phase shifts, and each operator allocates radio resources according to their phase-shift switching. Furthermore, we derive a quadratic unconstrained binary optimization equation for the proposed system to optimize radio resource allocation using quantum computing. The results of computer simulations indicate that the proposed system and method can be used to achieve efficient communication in 6G mobile communication systems.\",\"PeriodicalId\":13156,\"journal\":{\"name\":\"IEEE Transactions on Emerging Topics in Computing\",\"volume\":\"11 4\",\"pages\":\"950-961\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2023-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Emerging Topics in Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10179253/\",\"RegionNum\":2,\"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 Transactions on Emerging Topics in Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10179253/","RegionNum":2,"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}
Resource Allocation Optimization by Quantum Computing for Shared Use of Standalone IRS
Intelligent reflecting surfaces (IRSs) have attracted attention as a technology that can considerably improve the energy utilization efficiency of sixth-generation (6G) mobile communication systems. IRSs enable control of propagation characteristics by adjusting the phase shift of each reflective element. However, designing the phase shift requires the acquisition of channel information for each reflective element, which is impractical from an overhead perspective. In addition, for multiple wireless network operators to share an IRS for communication, new infrastructure facilities and operational costs are required at each operator's end to control the IRS in a coordinated manner. Herein, we propose a wireless communication system using standalone IRSs to solve these problems. The standalone IRSs cover a wide area by periodically switching phase shifts, and each operator allocates radio resources according to their phase-shift switching. Furthermore, we derive a quadratic unconstrained binary optimization equation for the proposed system to optimize radio resource allocation using quantum computing. The results of computer simulations indicate that the proposed system and method can be used to achieve efficient communication in 6G mobile communication systems.
期刊介绍:
IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.