On the Limits of Predictability in Real-World Radio Spectrum State Dynamics: From Entropy Theory to 5G Spectrum Sharing

被引：127

作者：

Ding, Guoru ^{[1
]}

Wang, Jinlong ^{[1
]}

Wu, Qihui ^{[1
]}

Yao, Yu-Dong ^{[2
]}

Li, Rongpeng ^{[3
]}

Zhang, Honggang ^{[4
]}

Zou, Yulong ^{[5
]}

机构：

[1] PLA Univ Sci & Technol, Nanjing, Jiangsu, Peoples R China

[2] Stevens Inst Technol, Hoboken, NJ 07030 USA

[3] Zhejiang Univ, Dept Informat Sci & Elect Engn, Hangzhou 310003, Zhejiang, Peoples R China

[4] Zhejiang Univ, Hangzhou 310003, Zhejiang, Peoples R China

[5] Nanjing Univ Posts & Telecommun, Nanjing, Jiangsu, Peoples R China

来源：

IEEE COMMUNICATIONS MAGAZINE | 2015年 / 53卷 / 07期

基金：

中国国家自然科学基金;

关键词：

PREDICTION;

D O I：

10.1109/MCOM.2015.7158283

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A range of applications in cognitive radio networks, from adaptive spectrum sensing to predictive spectrum mobility and dynamic spectrum access, depend on our ability to foresee the state evolution of radio spectrum, raising a fundamental question: To what degree is radio spectrum state (RSS) predictable? In this article we explore the fundamental limits of predictability in RSS dynamics by studying the RSS evolution patterns in spectrum bands of several popular services, including TV bands, ISM bands, cellular bands, and so on. From an information theory perspective, we introduce a methodology of using statistical entropy measures and Fano inequality to quantify the degree of predictability underlying real-world spectrum measurements. Despite the apparent randomness, we find a remarkable predictability, as large as 90 percent, in real-world RSS dynamics over a number of spectrum bands for all popular services. Furthermore, we discuss the potential applications of prediction-based spectrum sharing in 5G wireless communications.

引用

页码：178 / 183

页数：6

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