Game Theory-Based Channel Allocation in Cognitive Radio Networks

Shrivastav, Vani; Dhurandher, Sanjay Kumar; Woungang, Isaac; Kumar, Vinesh; Rodrigues, Joel J. P. C.

doi:10.1109/glocom.2016.7841855

Cited by 13 publications

(1 citation statement)

References 12 publications

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“…Markov decision process is proposed in [7] to maximize SUs throughput in a cognitive radio heterogeneous network. Many techniques, such as game theory based spectrum allocation [8], pricing, and auction mechanism based spectrum assignment [9][10][11], have been developed in the literature on spectrum assignment in CRHN. Multiobjective optimization is NPhard problem in cognitive radio networks [12].…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Interference and Cost in Cognitive Radio Heterogeneous Network Using Fuzzy Logic Powered Ants

Latif

Akraam

Saleem

2017

Wireless Communications and Mobile Computing

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The advent of emerging wireless technologies has increased the demand of spectrum resources. On the other hand, present spectrum assignment is fixed and underutilized. Cognitive radio (CR) provides good solution to spectrum scarcity problem to accommodate new wireless applications. The network selection is an important mechanism in cognitive radio heterogeneous network (CRHN) to provide optimal Quality of Service (QoS) to both Primary Users (PUs) and Secondary Users (SUs). The aim of this work is to provide optimal QoS to SUs by appropriate network selection and channel assignment. The proposed FLACSA selects the best network while maximizing the data rate and minimizing the interference and cost simultaneously. Simulation results show the attractive performance of our proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Joint Optimization of Interference and Cost in Cognitive Radio Heterogeneous Network Using Fuzzy Logic Powered Ants

Latif

Akraam

Saleem

2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

Interference Management in Cognitive Radio Networks

2021

Spectrum Sharing in Cognitive Radio Networks

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Simultaneous spectrum sensing, data transmission and Energy harvesting in multi‐channel cognitive sensor Networks with imperfect signal cancellation

Najimi

2020

Int J Communication

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SummaryIn multichannel cognitive sensor networks, the sensor users which have limited energy budgets sense the spectrum to determine the activity of the primary user. If the spectrum is idle, the sensor user can access the licensed spectrum. However, during the spectrum sensing, no data transmits. For improving the network throughput and saving more energy consumption, we propose the simultaneous spectrum sensing and data transmission scheme where the sensor receiver decodes the received signal, and from the remaining signal, the status of the channel (idle/busy) is determined. We also consider that the sensor users are powered by a radio‐frequency (RF) energy harvester. In this case, energy harvesting, data transmission, and spectrum sensing are done simultaneously. On the other hand, we select the proper sensor users for spectrum sensing and energy harvesting. We also allocate the best channels for data transmission simultaneously so that the network throughput maximizes and the constraints on the energy consumption and the detection performance are satisfied for each band. We formulate the problem and model it as a coalition game in which sensors act as game players and decide to make coalitions. Each coalition selects one of the channels to sense and transmit data, while the necessary detection probability and false alarm probability and also the energy consumption constraints are satisfied. The utility function of a coalition is proposed based on the energy consumption, false alarm probability, detection probability, and the network throughput. This paper proposes an efficient algorithm to reach a Nash‐stable coalition structure. It is demonstrated that the proposed method maximizes the network throughput and reduces the energy consumption while it provides sufficient detection quality, in comparison to other existent methods.

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Game Theory-Based Channel Allocation in Cognitive Radio Networks

Cited by 13 publications

References 12 publications

Joint Optimization of Interference and Cost in Cognitive Radio Heterogeneous Network Using Fuzzy Logic Powered Ants

Joint Optimization of Interference and Cost in Cognitive Radio Heterogeneous Network Using Fuzzy Logic Powered Ants

Interference Management in Cognitive Radio Networks

Simultaneous spectrum sensing, data transmission and Energy harvesting in multi‐channel cognitive sensor Networks with imperfect signal cancellation

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