TY - JOUR
T1 - On the limits of predictability in real-world radio spectrum state dynamics
T2 - From entropy theory to 5G spectrum sharing
AU - Ding, Guoru
AU - Wang, Jinlong
AU - Wu, Qihui
AU - Yao, Yu Dong
AU - Li, Rongpeng
AU - Zhang, Honggang
AU - Zou, Yulong
N1 - Publisher Copyright:
© 1979-2012 IEEE.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84937964101&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937964101&partnerID=8YFLogxK
U2 - 10.1109/MCOM.2015.7158283
DO - 10.1109/MCOM.2015.7158283
M3 - Article
AN - SCOPUS:84937964101
SN - 0163-6804
VL - 53
SP - 178
EP - 183
JO - IEEE Communications Magazine
JF - IEEE Communications Magazine
IS - 7
M1 - 7158283
ER -