  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 paper, 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, and Cellular bands, etc. 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%, in
the 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.
