Active Interference Constraint 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) operating based on an Adaptive Coding
and Modulation (ACM) protocol. The main idea is that the CRN
probes the PU and subsequently applies a Modulation and Coding
Classification (MCC) technique to acquire the Modulation and
Coding scheme (MCS) of the PU. This feedback is an implicit
channel state information (CSI) of the PU link, indicating how
harmful the probing induced interference is. The intelligence of
this sequential probing process lies on the selection of the power
levels of the Secondary Users (SUs) which aims to minimize the
number of probing attempts, a clearly Active Learning (AL)
procedure, and consequently the overall PU QoS degradation.
The enhancement introduced in this work is that we incorporate
the probability of each feedback being correct into this intelligent
probing mechanism by using a univariate Bayesian Nonparametric
AL method, the Probabilistic Bisection Algorithm (PBA). An
adaptation of the PBA is implemented for higher dimensions
and its effectiveness as an uncertainty driven AL method is
demonstrated through numerical simulations.