Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics

Predictability in spectrum prediction refers to the degree to which a correct prediction of the radio spectrum state (RSS) can be made quantitatively. It is obvious that the possibility that the future RSS is accurately predicted will be different when using different spectrum prediction algorithms....

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Main Authors: Jiachen Sun, Liang Shen, Guoru Ding, Rongpeng Li, Qihui Wu
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8089337/
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spelling doaj-2ec156b7555c436aa8dedb84f9ecca3c2021-03-29T19:56:28ZengIEEEIEEE Access2169-35362017-01-015227602277410.1109/ACCESS.2017.27660768089337Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data AnalyticsJiachen Sun0Liang Shen1Guoru Ding2https://orcid.org/0000-0003-1780-2547Rongpeng Li3https://orcid.org/0000-0003-4297-5060Qihui Wu4https://orcid.org/0000-0001-6796-8364College of Communications Engineering, Army Engineering University, Nanjing, ChinaCollege of Communications Engineering, Army Engineering University, Nanjing, ChinaCollege of Communications Engineering, Army Engineering University, Nanjing, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaPredictability in spectrum prediction refers to the degree to which a correct prediction of the radio spectrum state (RSS) can be made quantitatively. It is obvious that the possibility that the future RSS is accurately predicted will be different when using different spectrum prediction algorithms. However, the fundamental limits on the accuracy of various spectrum prediction algorithms should exist and be worthwhile to be paid attention to. In this paper, we define these fundamental limits as the performance bounds of predictability, which can be the important indexes when evaluating the performance of different spectrum prediction algorithms. Real-world spectrum data is involved to present comprehensive and profound analysis of the predictability. We first transform large amount of spectrum data into symbol sequences by sampling and quantization, to calculate the entropy of the symbol sequence, which represents the randomness of the RSS evolution. Then, we derive the upper bound and the lower bound of the predictability mainly from entropies of the symbol sequences. Further, we conduct the detailed analysis on the performance bounds of the predictability of the RSS. Based on real-world data analytics, the key insights among others include: 1) entropies almost have no relationship with selection of sampling intervals in the data preprocessing; 2) the upper and the lower bounds of the predictability will both decrease as the quantization level rises and tend to be stable around a value at last; and 3) two kinds of lower bounds of the predictability are proposed, and one of the lower bounds, the regularity R, can reveal the tidal effect of the evolution of the RSS.https://ieeexplore.ieee.org/document/8089337/Predictabilityspectrum stateentropy ratereal-world spectrum datadata analytics
collection DOAJ
language English
format Article
sources DOAJ
author Jiachen Sun
Liang Shen
Guoru Ding
Rongpeng Li
Qihui Wu
spellingShingle Jiachen Sun
Liang Shen
Guoru Ding
Rongpeng Li
Qihui Wu
Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics
IEEE Access
Predictability
spectrum state
entropy rate
real-world spectrum data
data analytics
author_facet Jiachen Sun
Liang Shen
Guoru Ding
Rongpeng Li
Qihui Wu
author_sort Jiachen Sun
title Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics
title_short Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics
title_full Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics
title_fullStr Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics
title_full_unstemmed Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics
title_sort predictability analysis of spectrum state evolution: performance bounds and real-world data analytics
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2017-01-01
description Predictability in spectrum prediction refers to the degree to which a correct prediction of the radio spectrum state (RSS) can be made quantitatively. It is obvious that the possibility that the future RSS is accurately predicted will be different when using different spectrum prediction algorithms. However, the fundamental limits on the accuracy of various spectrum prediction algorithms should exist and be worthwhile to be paid attention to. In this paper, we define these fundamental limits as the performance bounds of predictability, which can be the important indexes when evaluating the performance of different spectrum prediction algorithms. Real-world spectrum data is involved to present comprehensive and profound analysis of the predictability. We first transform large amount of spectrum data into symbol sequences by sampling and quantization, to calculate the entropy of the symbol sequence, which represents the randomness of the RSS evolution. Then, we derive the upper bound and the lower bound of the predictability mainly from entropies of the symbol sequences. Further, we conduct the detailed analysis on the performance bounds of the predictability of the RSS. Based on real-world data analytics, the key insights among others include: 1) entropies almost have no relationship with selection of sampling intervals in the data preprocessing; 2) the upper and the lower bounds of the predictability will both decrease as the quantization level rises and tend to be stable around a value at last; and 3) two kinds of lower bounds of the predictability are proposed, and one of the lower bounds, the regularity R, can reveal the tidal effect of the evolution of the RSS.
topic Predictability
spectrum state
entropy rate
real-world spectrum data
data analytics
url https://ieeexplore.ieee.org/document/8089337/
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AT guoruding predictabilityanalysisofspectrumstateevolutionperformanceboundsandrealworlddataanalytics
AT rongpengli predictabilityanalysisofspectrumstateevolutionperformanceboundsandrealworlddataanalytics
AT qihuiwu predictabilityanalysisofspectrumstateevolutionperformanceboundsandrealworlddataanalytics
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