### 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.

Original language | English |
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Title of host publication | Proceedings - IEEE INFOCOM |

Pages | 2114-2122 |

Number of pages | 9 |

DOIs | |

State | Published - Oct 12 2009 |

Event | 28th Conference on Computer Communications, IEEE INFOCOM 2009 - Rio de Janeiro, Brazil Duration: Apr 19 2009 → Apr 25 2009 |

### Other

Other | 28th Conference on Computer Communications, IEEE INFOCOM 2009 |
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Country | Brazil |

City | Rio de Janeiro |

Period | 4/19/09 → 4/25/09 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)
- Electrical and Electronic Engineering

### Cite this

*Proceedings - IEEE INFOCOM*(pp. 2114-2122). [5062135] https://doi.org/10.1109/INFCOM.2009.5062135

**Coalitional games for distributed collaborative spectrum sensing in cognitive radio networks.** / Saad, Walid; Han, Zhu; Debbah, Mérouane; Hjørungnes, Are; Başar, Tamer.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings - IEEE INFOCOM.*, 5062135, pp. 2114-2122, 28th Conference on Computer Communications, IEEE INFOCOM 2009, Rio de Janeiro, Brazil, 4/19/09. https://doi.org/10.1109/INFCOM.2009.5062135

}

TY - GEN

T1 - Coalitional games for distributed collaborative spectrum sensing in cognitive radio networks

AU - Saad, Walid

AU - Han, Zhu

AU - Debbah, Mérouane

AU - Hjørungnes, Are

AU - Başar, Tamer

PY - 2009/10/12

Y1 - 2009/10/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=70349185622&partnerID=8YFLogxK

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U2 - 10.1109/INFCOM.2009.5062135

DO - 10.1109/INFCOM.2009.5062135

M3 - Conference contribution

SN - 9781424435135

SP - 2114

EP - 2122

BT - Proceedings - IEEE INFOCOM

ER -