无线局域网频谱使用的时域确定性-随机模型研究

K. Umebayashi, M. López-Benítez
{"title":"无线局域网频谱使用的时域确定性-随机模型研究","authors":"K. Umebayashi, M. López-Benítez","doi":"10.1109/WCNC.2018.8377445","DOIUrl":null,"url":null,"abstract":"Duty cycle (DC) has been used to express the deterministic and stochastic aspects of spectrum usage. Specifically, a deterministic model for the mean of the duty cycle (MDC) has been proposed in a previous work. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is used to express the randomness. In this paper, we extend the conventional approach to a combined deterministic-stochastic (DS) model which represents both the deterministic and stochastic aspects. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in spectrum usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model to reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive spectrum measurements in IEEE 802.11-based wireless local area networks.","PeriodicalId":360054,"journal":{"name":"2018 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"505 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A study on time domain deterministic-stochastic model of spectrum usage in WLAN\",\"authors\":\"K. Umebayashi, M. López-Benítez\",\"doi\":\"10.1109/WCNC.2018.8377445\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Duty cycle (DC) has been used to express the deterministic and stochastic aspects of spectrum usage. Specifically, a deterministic model for the mean of the duty cycle (MDC) has been proposed in a previous work. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is used to express the randomness. In this paper, we extend the conventional approach to a combined deterministic-stochastic (DS) model which represents both the deterministic and stochastic aspects. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in spectrum usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model to reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive spectrum measurements in IEEE 802.11-based wireless local area networks.\",\"PeriodicalId\":360054,\"journal\":{\"name\":\"2018 IEEE Wireless Communications and Networking Conference (WCNC)\",\"volume\":\"505 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Wireless Communications and Networking Conference (WCNC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCNC.2018.8377445\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Wireless Communications and Networking Conference (WCNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCNC.2018.8377445","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

占空比(DC)被用来表示频谱使用的确定性和随机性。具体地说,一个确定性模型的平均占空比(MDC)已经在以前的工作中提出。另一方面,短时间内观测到的DC (O-DC)具有随机性,并采用随机模型来表示这种随机性。在本文中,我们将传统的方法扩展到一个同时表示确定性和随机方面的组合确定性-随机(DS)模型。对于O-DC的分布,beta分布已被用作随机模型,但我们采用了混合的beta分布。混合-beta分布在频谱使用测量中可以达到更高的精度,但由于它比beta分布具有更多的参数,因此需要更多的数据存储容量。对于这个问题,我们在ds模型中采用回归分析,在保持精度的同时减少参数的数量。我们在基于IEEE 802.11的无线局域网中展示了基于穷举频谱测量的ds模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A study on time domain deterministic-stochastic model of spectrum usage in WLAN
Duty cycle (DC) has been used to express the deterministic and stochastic aspects of spectrum usage. Specifically, a deterministic model for the mean of the duty cycle (MDC) has been proposed in a previous work. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is used to express the randomness. In this paper, we extend the conventional approach to a combined deterministic-stochastic (DS) model which represents both the deterministic and stochastic aspects. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in spectrum usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model to reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive spectrum measurements in IEEE 802.11-based wireless local area networks.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信