{"title":"Spectrum usage model for smart spectrum","authors":"K. Umebayashi","doi":"10.1109/ICCChinaW.2019.8849939","DOIUrl":null,"url":null,"abstract":"This paper focuses on a spectrum usage model in the time domain in the context of dynamic spectrum access (DSA). To achieve a sophisticated dynamic spectrum access, understanding the spectrum usage is an important task. We focus on duty cycle (DC) as a feature quantity of spectrum usage in the time domain and observed DC (O-DC) obtained from long-term spectrum measurement results is used for the modeling. In fact, O-DC has stochastic and deterministic behaviors and we have been investigated modeling for both behaviors. O-DC is stochastic behavior and a mixture distribution based modeling has been considered for the model of stochastic behavior. We employ nonparametric Bayesian model (NPBM) in which the number of distributions is also an adjustable parameter. Statistics of O-DC, such as mean of O-DC, has a deterministic behavior in time domain. Specifically, the deterministic behavior is determined by the common daily habits, such as mean of O-DC during is night is low, but it is high during daytime. We show the validity of the stochastic and deterministic model for O-DC based on long-term spectrum measurement results.","PeriodicalId":252172,"journal":{"name":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCChinaW.2019.8849939","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

This paper focuses on a spectrum usage model in the time domain in the context of dynamic spectrum access (DSA). To achieve a sophisticated dynamic spectrum access, understanding the spectrum usage is an important task. We focus on duty cycle (DC) as a feature quantity of spectrum usage in the time domain and observed DC (O-DC) obtained from long-term spectrum measurement results is used for the modeling. In fact, O-DC has stochastic and deterministic behaviors and we have been investigated modeling for both behaviors. O-DC is stochastic behavior and a mixture distribution based modeling has been considered for the model of stochastic behavior. We employ nonparametric Bayesian model (NPBM) in which the number of distributions is also an adjustable parameter. Statistics of O-DC, such as mean of O-DC, has a deterministic behavior in time domain. Specifically, the deterministic behavior is determined by the common daily habits, such as mean of O-DC during is night is low, but it is high during daytime. We show the validity of the stochastic and deterministic model for O-DC based on long-term spectrum measurement results.
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智能频谱的频谱使用模型
本文研究了动态频谱接入(DSA)环境下的时域频谱使用模型。为了实现复杂的动态频谱接入,了解频谱的使用情况是一个重要的任务。我们将占空比(DC)作为时域频谱使用的特征量,并使用从长期频谱测量结果中获得的观测DC (O-DC)进行建模。事实上,O-DC具有随机和确定性行为,我们已经研究了这两种行为的建模。O-DC是随机行为,随机行为模型考虑了基于混合分布的建模方法。我们采用非参数贝叶斯模型(NPBM),其中分布数也是一个可调参数。O-DC的统计量,如O-DC的均值,在时域上具有确定性行为。具体而言,确定性行为是由共同的日常习惯决定的,如夜间O-DC均值较低,而白天较高。基于长期频谱测量结果,我们证明了O-DC随机和确定性模型的有效性。
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