Interference Constraint Active Learning with Uncertain Feedback for Cognitive Radio Networks

In this paper, an intelligent probing method for
interference constraint learning is proposed to allow a centralized
cognitive radio network (CRN) to access the frequency band of
a primary user (PU) in an underlay cognitive communication
scenario. 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 is implicit channel
state information of the PU link, indicating whether the probinginduced
interference is harmful or not. The intelligence of this
sequential probing process lies in the selection of the power levels
of the secondary users, which aims to minimize the number of
probing attempts, a clearly active learning (AL) procedure, and
expectantly the overall PU QoS degradation. The enhancement
introduced in this paper is that we incorporate the probability
of each feedback being correct into this intelligent probing
mechanism by using a multivariate Bayesian AL method. This
technique is inspired by the probabilistic bisection algorithm and
the deterministic cutting plane methods (CPMs). The optimality
of this multivariate Bayesian AL method is proven and its
effectiveness is demonstrated through numerical simulations.
Computationally cheap CPM adaptations are also presented,
which outperform existing AL methods.