Coalitional Games for Distributed Collaborative Spectrum Sensing in Cognitive Radio Networks (0905.2659v1)

Abstract: Collaborative spectrum sensing among secondary users (SUs) in cognitive networks is shown to yield a significant performance improvement. However, there exists an inherent trade off between the gains in terms of probability of detection of the primary user (PU) and the costs in terms of false alarm probability. In this paper, we study the impact of this trade off on the topology and the dynamics of a network of SUs seeking to reduce the interference on the PU through collaborative sensing. Moreover, while existing literature mainly focused on centralized solutions for collaborative sensing, we propose distributed collaboration strategies through game theory. We model the problem as a non-transferable coalitional game, and propose a distributed algorithm for coalition formation through simple merge and split rules. Through the proposed algorithm, SUs can autonomously collaborate and self-organize into disjoint independent coalitions, while maximizing their detection probability taking into account the cooperation costs (in terms of false alarm). We study the stability of the resulting network structure, and show that a maximum number of SUs per formed coalition exists for the proposed utility model. Simulation results show that the proposed algorithm allows a reduction of up to 86.6% of the average missing probability per SU (probability of missing the detection of the PU) relative to the non-cooperative case, while maintaining a certain false alarm level. In addition, through simulations, we compare the performance of the proposed distributed solution with respect to an optimal centralized solution that minimizes the average missing probability per SU. Finally, the results also show how the proposed algorithm autonomously adapts the network topology to environmental changes such as mobility.

• The paper models collaborative spectrum sensing in cognitive radio networks as a non-transferable utility coalitional game and proposes a distributed algorithm for secondary users to form coalitions autonomously.
• A key result shows the proposed distributed algorithm reduces the average missing probability per secondary user by up to 86.6% compared to non-cooperative sensing, while maintaining a prescribed false alarm rate.
• Analysis reveals that the size of the self-forming coalitions is explicitly bounded by a derived upper limit, ensuring the benefits of distributed sensing are maintained without excessive false alarms.

Coalitional Games for Distributed Collaborative Spectrum Sensing in Cognitive Radio Networks: An Overview

This paper presents a comprehensive paper on collaborative spectrum sensing in cognitive radio networks with a focus on distributed coalition formation among secondary users (SUs) utilizing coalitional game theory. The research addresses the trade-off between enhancing the probability of detection for primary users (PUs) and minimizing false alarm probabilities in a distributed setting, a challenge that is pivotal when the goal is to efficiently utilize spectrum holes without centralized control.

Core Contributions

The primary contributions of this paper include modeling collaborative spectrum sensing as a non-transferable utility coalitional game and proposing an autonomous, distributed algorithm for coalition formation among SUs. The game-theoretic approach ensures that SUs can self-organize into coalitions without centralized coordination, thereby overcoming the overhead and complexity often associated with centralized solutions.

The authors approach this problem by introducing a utility function that considers both the benefit of increased detection probability and the cost incurred by false alarms. The game is shown to not always result in a grand coalition due to the inherent costs associated with increased false alarms, leading to the formation of multiple independent disjoint coalitions.

Key Results and Analysis

A notable outcome from the simulation results is that the proposed algorithm can reduce the average missing probability per SU by up to 86.6% compared to the non-cooperative scenario, maintaining a prescribed level of false alarm probability. This dramatic reduction underscores the potential efficiency gains achievable through distributed cooperation.

Furthermore, the analysis reveals that the size of forming coalitions is explicitly bound by a derived upper limit, $M_{\max}$, dependent on the system parameters such as the false alarm constraint and non-cooperative false alarm probability. This ensures that coalitions do not grow excessively large, which would counteract the benefits of distributed sensing through excessive false alarms.

Implications and Future Directions

The introduction of distributed coalition formation algorithms has practical implications in deploying cognitive radio networks, especially when centralized control is not feasible. By enabling SUs to dynamically form and reconfigure coalitions based on local network conditions and requirements, the method augments the flexibility and resilience of cognitive radio operations.

The theoretical framework and algorithms presented set the stage for several future research directions. Potential extensions include adapting the game model to account for more intricate dynamics such as heterogeneous SU capabilities, multi-tier cognitive radio networks, and real-time environment changes in more complex mobility models. Additionally, exploring the trade-offs between computational complexity and the accuracy of coalition formation in large-scale networks could provide deeper insights for practical applications.

Overall, the methodology and results discussed in this paper make a significant contribution to the field of cognitive radio, offering a robust approach for handling the distributed nature of spectrum sensing with minimized false alarm impact. By framing the problem within a coalitional game theory context, the authors advance the understanding of cooperative behavior and its advantages in cognitive radio networks.