Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

TL;DR

This paper introduces a Bayesian active learning approach for efficiently learning interference constraints in cognitive radio networks, optimizing probing strategies while protecting primary users.

Contribution

It presents a novel constrained Bayesian active learning method using Expectation Propagation for interference constraint learning in cognitive radio networks.

Findings

Achieves accurate interference constraint learning with fewer probing attempts.

Effectively limits harmful interference events during learning.

Outperforms previous active learning methods in simulations.

Abstract

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.