A Cooperative Bayesian Nonparametric Framework for Primary User Activity Monitoring in Cognitive Radio Network

TL;DR

This paper presents a cooperative Bayesian nonparametric framework for cognitive radio devices to estimate primary user activity patterns more accurately through coalition formation and Bayesian models, adapting to changing distributions.

Contribution

It introduces a novel coalition formation algorithm enabling cognitive nodes to cooperatively estimate unknown primary user activity distributions using Bayesian nonparametrics.

Findings

Significant reduction in estimation error, up to 36.5% in KL divergence.

Effective adaptation to changing primary user distributions.

Enhanced accuracy over non-cooperative methods.

Abstract

This paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users(PUs), to cooperate and use \emph{Bayesian nonparametric} techniques to estimate the distributions of the PUs' activity pattern, assumed to be completely unknown. In the proposed model, each cognitive node may have its own individual view on each PU's distribution, and, hence, seeks to find partners having a correlated perception. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use a combination of Bayesian nonparametric models such as the Dirichlet process and statistical goodness of fit techniques in order to improve the accuracy of the estimated PUs' activity distributions. Simulation results show that the proposed algorithm significantly improves the estimates of the PUs' distributions and yields a performance advantage, in terms of reduction of the average achieved Kullback-Leibler distance between the real and the estimated distributions, reaching up to 36.5% relative the non-cooperative estimates. The results also show that the proposed algorithm enables the cognitive nodes to adapt their cooperative decisions when the actual PUs' distributions change due to, for example, PU mobility.