Distributed Cooperative Sensing in Cognitive Radio Networks: An Overlapping Coalition Formation Approach

TL;DR

This paper introduces a novel distributed cooperative sensing algorithm for cognitive radio networks that allows secondary users to form overlapping coalitions, significantly improving sensing performance and reducing errors and overhead.

Contribution

It proposes a new overlapping coalition formation game approach enabling secondary users to self-organize into overlapping coalitions for enhanced spectrum sensing.

Findings

Error probability decreased by up to 25%

Missed detection probability reduced by up to 20%

Overhead reduced by up to 80%

Abstract

Cooperative spectrum sensing has been shown to yield a significant performance improvement in cognitive radio networks. In this paper, we consider distributed cooperative sensing (DCS) in which secondary users (SUs) exchange data with one another instead of reporting to a common fusion center. In most existing DCS algorithms, the SUs are grouped into disjoint cooperative groups or coalitions, and within each coalition the local sensing data is exchanged. However, these schemes do not account for the possibility that an SU can be involved in multiple cooperative coalitions thus forming overlapping coalitions. Here, we address this problem using novel techniques from a class of cooperative games, known as overlapping coalition formation games, and based on the game model, we propose a distributed DCS algorithm in which the SUs self-organize into a desirable network structure with overlapping coalitions. Simulation results show that the proposed overlapping algorithm yields significant performance improvements, decreasing the total error probability up to 25% in the Q_m+Q_f criterion, the missed detection probability up to 20% in the Q_m/Q_f criterion, the overhead up to 80%, and the total report number up to 10%, compared with the state-of-the-art non-overlapping algorithm.