  Spectrum sensing in a large-scale heterogeneous network is very challenging
as it usually requires a large number of static secondary users (SUs) to obtain
the global spectrum states. To tackle this problem, in this paper, we propose a
new framework based on Bayesian machine learning. We exploit the mobility of
multiple SUs to simultaneously collect spectrum sensing data, and cooperatively
derive the global spectrum states. We first develop a novel non-parametric
Bayesian learning model, referred to as beta process sticky hidden Markov model
(BP-SHMM), to capture the spatial-temporal correlation in the collected
spectrum data, where SHMM models the latent statistical correlation within each
mobile SU's time series data, while BP realizes the cooperation among multiple
SUs. Bayesian inference is then carried out to automatically infer the
heterogeneous spectrum states. Based on the inference results, we also develop
a new algorithm with a refinement mechanism to predict the spectrum
availability, which enables a newly joining SU to immediately access the
unoccupied frequency band without sensing. Simulation results show that the
proposed framework can significantly improve spectrum sensing performance
compared with the existing spectrum sensing techniques.
