Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

In this paper, a sequential probing method for
interference constraint learning is proposed to allow a centralized
Cognitive Radio Network (CRN) accessing the frequency band
of a Primary User (PU) in an underlay cognitive scenario with a
designed PU protection specification. The main idea is that the
CRN probes the PU and subsequently eavesdrops the reverse PU
link to acquire the binary ACK/NACK packet. This feedback
indicates whether the probing-induced interference is harmful or
not and can be used to learn the PU interference constraint. The
cognitive part of this sequential probing process is the selection
of the power levels of the Secondary Users (SUs) which aims to
learn the PU interference constraint with a minimum number of
probing attempts while setting a limit on the number of harmful
probing-induced interference events or equivalently of NACK
packet observations over a time window. This constrained design
problem is studied within the Active Learning (AL) framework
and an optimal solution is derived and implemented with a
sophisticated, accurate and fast Bayesian Learning method, the
Expectation Propagation (EP). The performance of this solution is
also demonstrated through numerical simulations and compared
with modified versions of AL techniques we developed in earlier
work.