{"title":"Communication Networks Opportunistic multiuser scheduling with reduced feedback load","authors":"Y. Al-Harthi","doi":"10.1002/ett.1403","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a reduced feedback opportunistic scheduling (RFOS) algorithm that reduces the feedback load while preserving the performance of opportunistic scheduling (OS). The RFOS algorithm is a modified version of our previously proposed algorithm, the DSMUDiv algorithm. The main difference is that RFOS consists of a probing process (search process) and a requesting feedback process based on a threshold. The threshold value is variable, and it depends on the probing process. To reduce the feedback rate, a quantised value indicating the modulation level is fed back, instead of the full value of the signal-to-noise ratio (SNR), which we call quantised SNR. The paper includes the closed-form expressions of the probing load, feedback load and spectral efficiency. In addition, we investigate the effect of the scheduling delay on the system throughput (STH). Under slow Rayleigh fading assumption, we compare RFOS algorithm with the DSMUDiv and optimal (full feedback load) selective diversity scheduling algorithms. Copyright © 2010 John Wiley & Sons, Ltd.","PeriodicalId":50473,"journal":{"name":"European Transactions on Telecommunications","volume":"21 1","pages":"299-311"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Transactions on Telecommunications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/ett.1403","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
通信网络减少反馈负载的机会多用户调度
本文提出了一种减少反馈机会调度(RFOS)算法,在保留机会调度(OS)性能的同时减少了反馈负载。RFOS算法是我们之前提出的算法(DSMUDiv算法)的改进版本。主要区别在于RFOS由探测过程(搜索过程)和基于阈值的请求反馈过程组成。阈值是可变的,它取决于探测过程。为了降低反馈速率,反馈的是一个量化值,表示调制电平,而不是信噪比(SNR)的全部值,我们称之为量化信噪比。本文给出了探测载荷、反馈载荷和频谱效率的封闭表达式。此外,我们还研究了调度延迟对系统吞吐量(STH)的影响。在慢瑞利衰落假设下,将RFOS算法与DSMUDiv算法和最优(全反馈负载)选择性分集调度算法进行了比较。版权所有©2010 John Wiley & Sons, Ltd
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