Coalitional Games in Partition Form for Joint Spectrum Sensing and Access in Cognitive Radio Networks

TL;DR

This paper models the tradeoff between spectrum sensing and access in cognitive radio networks as a coalitional game, proposing a distributed algorithm for secondary users to self-organize into coalitions that optimize their spectrum utilization.

Contribution

It introduces a novel coalition formation algorithm in partition form for secondary users to cooperatively optimize spectrum sensing and access in cognitive radio networks.

Findings

Coalitional game model effectively captures spectrum sensing and access tradeoff.

Proposed algorithm enables distributed coalition formation among secondary users.

Simulation shows improved performance and adaptability over non-cooperative strategies.

Abstract

Unlicensed secondary users (SUs) in cognitive radio networks are subject to an inherent tradeoff between spectrum sensing and spectrum access. Although each SU has an incentive to sense the primary user (PU) channels for locating spectrum holes, this exploration of the spectrum can come at the expense of a shorter transmission time, and, hence, a possibly smaller capacity for data transmission. This paper investigates the impact of this tradeoff on the cooperative strategies of a network of SUs that seek to cooperate in order to improve their view of the spectrum (sensing), reduce the possibility of interference among each other, and improve their transmission capacity (access). The problem is modeled as a coalitional game in partition form and an algorithm for coalition formation is proposed. Using the proposed algorithm, the SUs can make individual distributed decisions to join or leave a coalition while maximizing their utilities which capture the average time spent for sensing as well as the capacity achieved while accessing the spectrum. It is shown that, by using the proposed algorithm, the SUs can self-organize into a network partition composed of disjoint coalitions, with the members of each coalition cooperating to jointly optimize their sensing and access performance. Simulation results show the performance improvement that the proposed algorithm yields with respect to the non-cooperative case. The results also show how the algorithm allows the SUs to self-adapt to changes in the environment such as the change in the traffic of the PUs, or slow mobility.