{"title":"Retransmission scheduling in 802.15.4e LLDN - a reinforcement learning approach with relayers","authors":"A. Willig, Yakir Matusovsky, A. Kind","doi":"10.1109/ATNAC.2016.7878784","DOIUrl":null,"url":null,"abstract":"We consider the scheduling of retransmissions in the low-latency deterministic network (LLDN) extension to the IEEE 802.15.4 standard. We propose a number of retransmission schemes with varying degree of required changes to the LLDN specification. In particular, we propose a retransmission scheme which uses cooperative relayers and where the best relayer for a source node is learned using reinforcement-learning method. The method allows to adapt relayer selections in face of time-varying channels. Our results show that the relayer-based methods achieve a much better reliability over the other methods, both over static (but unknown) and over time-varying channels.","PeriodicalId":317649,"journal":{"name":"2016 26th International Telecommunication Networks and Applications Conference (ITNAC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 26th International Telecommunication Networks and Applications Conference (ITNAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATNAC.2016.7878784","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
We consider the scheduling of retransmissions in the low-latency deterministic network (LLDN) extension to the IEEE 802.15.4 standard. We propose a number of retransmission schemes with varying degree of required changes to the LLDN specification. In particular, we propose a retransmission scheme which uses cooperative relayers and where the best relayer for a source node is learned using reinforcement-learning method. The method allows to adapt relayer selections in face of time-varying channels. Our results show that the relayer-based methods achieve a much better reliability over the other methods, both over static (but unknown) and over time-varying channels.