{"title":"Caching for Doubly Selective Fading Channels via Model-Agnostic Meta-Reinforcement Learning","authors":"Weibao He;Fasheng Zhou;Dong Tang;Fang Fang;Wei Chen","doi":"10.1109/JSYST.2024.3442958","DOIUrl":null,"url":null,"abstract":"Edge caching is expected to alleviate the traffic consumption in next-generation communications. In this article, we consider the transmission delay in wideband communications deteriorated by rapid user movements, where the frequency-selective wideband fading channels become fast time-varying and hence \n<italic>doubly-selective</i>\n due to the user movements. To preferably allocate the caching resource in such circumstance, we introduce a coordinated caching network and accordingly formulate an allocation problem. However, the formulated problem is shown to be NP-hard. By considering the extremely high computational complexity to solve the NP-hard problem by traditional optimization algorithm, and considering only a few samples can be obtained for each training instance due to shortened coherence-time in the dynamical doubly selective fading channels, we propose a model-agnostic meta-reinforcement learning method to address the formulated problem. Particularly, the proposed method can efficiently recognize the unstable mobile channels and accordingly cache to reduce the overall transmission delay while only requires a few training samples. Numerical simulations are performed to verify the effectiveness of the proposed method and results show that the proposed one outperforms the commonly adopted existing method of deep-deterministic-policy-gradient learning in terms of average delay and cache hit rate.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1776-1785"},"PeriodicalIF":4.0000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Systems Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10643614/","RegionNum":3,"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}
引用次数: 0
Abstract
Edge caching is expected to alleviate the traffic consumption in next-generation communications. In this article, we consider the transmission delay in wideband communications deteriorated by rapid user movements, where the frequency-selective wideband fading channels become fast time-varying and hence
doubly-selective
due to the user movements. To preferably allocate the caching resource in such circumstance, we introduce a coordinated caching network and accordingly formulate an allocation problem. However, the formulated problem is shown to be NP-hard. By considering the extremely high computational complexity to solve the NP-hard problem by traditional optimization algorithm, and considering only a few samples can be obtained for each training instance due to shortened coherence-time in the dynamical doubly selective fading channels, we propose a model-agnostic meta-reinforcement learning method to address the formulated problem. Particularly, the proposed method can efficiently recognize the unstable mobile channels and accordingly cache to reduce the overall transmission delay while only requires a few training samples. Numerical simulations are performed to verify the effectiveness of the proposed method and results show that the proposed one outperforms the commonly adopted existing method of deep-deterministic-policy-gradient learning in terms of average delay and cache hit rate.
期刊介绍:
This publication provides a systems-level, focused forum for application-oriented manuscripts that address complex systems and system-of-systems of national and global significance. It intends to encourage and facilitate cooperation and interaction among IEEE Societies with systems-level and systems engineering interest, and to attract non-IEEE contributors and readers from around the globe. Our IEEE Systems Council job is to address issues in new ways that are not solvable in the domains of the existing IEEE or other societies or global organizations. These problems do not fit within traditional hierarchical boundaries. For example, disaster response such as that triggered by Hurricane Katrina, tsunamis, or current volcanic eruptions is not solvable by pure engineering solutions. We need to think about changing and enlarging the paradigm to include systems issues.