Convergence of cognitive radio networks

Neel, J.; Reed, Jeffrey H.; Gilles, Robert P.

doi:10.1109/wcnc.2004.1311438

Cited by 133 publications

(89 citation statements)

References 10 publications

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“…Some of the seminal work in applying potential games to cognitive radio problems was done by Neel [29]. A number of problems in multi-channel communications can be modeled as potential games.…”

Section: ) Repeated Gamesmentioning

confidence: 99%

Learning and Reasoning in Cognitive Radio Networks

Gavrilovska

Atanasovski

Macaluso

et al. 2013

IEEE Commun. Surv. Tutorials

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Abstract-Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning. This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc. The majority of overview works in the field of cognitive radio networks deal with the notions of observation and adaptations, which are not a distinguished cognitive radio networking aspect. Therefore, this paper provides insight into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions.

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Section: ) Repeated Gamesmentioning

confidence: 99%

Learning and Reasoning in Cognitive Radio Networks

Gavrilovska

Atanasovski

Macaluso

et al. 2013

IEEE Commun. Surv. Tutorials

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“…Some others concentrate the existence and uniqueness of the equilibrium solution. For example, the investigations in the potential game (18) and the super-modular game (6), which are all game models with some nice properties guaranteeing the existence and uniqueness. Actually, this is guaranteed by the specific utility function design in the game model (6-9; 18).…”

Section: Background Of Game Theory For Wireless Communicationsmentioning

confidence: 99%

“…Adaptability: the radio is capable of changing its waveform. Intelligence: the radio is capable of applying information towards a purposeful goal (6). The interference avoidance problem between the primary user and the secondary user is a critical issue for the cognitive radio networks.…”

Section: Introductionmentioning

confidence: 99%

Power Control for Cognitive Radios: A Mixed-Strategy Game-Theoretic Framework

Yang¹,

Li²

2012

Foundation of Cognitive Radio Systems

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“…Existing spectrum allocation methods are game theorybased spectrum assignment model [4][5][6], graph coloring theorybased spectrum assignment model [7][8][9] and auction theorybased spectrum assignment model [10][11][12] and etc.. Although the model based on game theory and auction theory is very complete, but difficult to execute, while due to the fast convergence speed, execute easily and flexible expanding, the model based on graph coloring theory has caused wide research.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Radio Spectrum Assignment Based on Invasive Weed Optimization Algorithm

Xie¹,

Li²,

Zhu³

et al. 2018

Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)

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Abstract-According to the problem of the optimization of overall performance of idle spectrum assignment for cognitive radio, it proposed a spectrum assignment method for cognitive radio based on binary weed algorithm. Considering maximizing total network efficiency and fair effectiveness as a criterion, the algorithm is designed and implementation steps are presented. Simulation and analysis compares the solution of the spectrum assignment with Genetic Algorithm and particle swarm algorithm. The results show that the spectrum assignment algorithm based on binary invasive weed algorithm has a higher performance. It can achieve maximizing sum reward and proportional fair better and receiving a better optimal solution.

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Convergence of cognitive radio networks

Cited by 133 publications

References 10 publications

Learning and Reasoning in Cognitive Radio Networks

Learning and Reasoning in Cognitive Radio Networks

Power Control for Cognitive Radios: A Mixed-Strategy Game-Theoretic Framework

Cognitive Radio Spectrum Assignment Based on Invasive Weed Optimization Algorithm

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