Stochastic approach for channel selection in cognitive radio networks using optimization techniques

Cognitive Radio (CR) is a versatile, insightful radio and system innovation that can naturally recognize accessible directs in a remote range and change transmission parameters empowering more interchanges to run simultaneously and furthermore improve radio working conduct. The primary thought of the intellectual system is to give the range band to the unlicensed clients without making any damage to the earth. By utilizing the recurrence range band, we came to utilization of these intellectual systems. Right off the bat, we have to frame the system through any methodologies .we are utilizing optimization Mechanisms for path identification in cognitive Radio.

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QoS-guaranteed channel selection scheme for cognitive radio networks with variable channel bandwidths

2009 International Conference on Communications, Circuits and Systems ◽

10.1109/icccas.2009.5250552 ◽

2009 ◽

Cited By ~ 12

Author(s):

Samer T. Talat ◽

Li-Chun Wang

Keyword(s):

Cognitive Radio ◽

Cognitive Radio Networks ◽

Channel Selection ◽

Radio Networks ◽

Selection Scheme ◽

Channel Bandwidths

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On Fuzzy Logic-Based Channel Selection in Cognitive Radio Networks

Advances in Wireless Technologies and Telecommunication - Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management ◽

10.4018/978-1-4666-6571-2.ch010 ◽

2015 ◽

pp. 255-274 ◽

Cited By ~ 1

Author(s):

Yong Yao ◽

Alexandru Popescu ◽

Adrian Popescu

Keyword(s):

Fuzzy Logic ◽

Cognitive Radio ◽

Cognitive Radio Networks ◽

Environmental Changes ◽

Radio Channel ◽

New Technology ◽

Channel Selection ◽

Decision Criterion ◽

Radio Networks ◽

Competition Level

Cognitive radio networks are a new technology based on which unlicensed users are allowed access to licensed spectrum under the condition that the interference perceived by licensed users is minimal. That means unlicensed users need to learn from environmental changes and to make appropriate decisions regarding the access to the radio channel. This is a process that can be done by unlicensed users in a cooperative or non-cooperative way. Whereas the non-cooperative algorithms are risky with regard to performance, the cooperative algorithms have the capability to provide better performance. This chapter shows a new fuzzy logic-based decision-making algorithm for channel selection. The underlying decision criterion considers statistics of licensed user channel occupancy as well as information about the competition level of unlicensed users. The theoretical studies indicate that the unlicensed users can obtain an efficient sharing of the available channels. Simulation results are reported to demonstrate the performance and effectiveness of the suggested algorithm.

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On the Use of Particle Swarm Optimization Techniques for Channel Assignments in Cognitive Radio Networks

Multidisciplinary Computational Intelligence Techniques ◽

10.4018/978-1-4666-1830-5.ch012 ◽

2012 ◽

pp. 202-214 ◽

Cited By ~ 1

Author(s):

Hisham M. Abdelsalam ◽

Haitham S. Hamza ◽

Abdoulraham M. Al-Shaar ◽

Abdelbaset S. Hamza

Keyword(s):

Cognitive Radio ◽

Cognitive Radio Networks ◽

Channel Assignment ◽

Assignment Problem ◽

Pso Algorithm ◽

Radio Networks ◽

Optimization Techniques ◽

Secondary Users ◽

Swarm Optimization ◽

Channel Assignment Problem

Efficient utilization of open spectrum in cognitive radio networks requires appropriate allocation of idle spectrum frequency bands (not used by licensed users) among coexisting cognitive radios (secondary users) while minimizing interference among all users. This problem is referred to as the spectrum allocation or the channel assignment problem in cognitive radio networks, and is shown to be NP-hard. Accordingly, different optimization techniques based on evolutionary algorithms were needed in order to solve the channel assignment problem. This chapter investigates the use of particular swarm optimization (PSO) techniques to solve the channel assignment problem in cognitive radio networks. In particular, the authors study the definitiveness of using the native PSO algorithm and the Improved Binary PSO (IBPSO) algorithm to solve the assignment problem. In addition, the performance of these algorithms is compared to that of a fine-tuned genetic algorithm (GA) for this particular problem. Three utilization functions, namely, Mean-Reward, Max-Min-Reward, and Max-Proportional-Fair, are used to evaluate the effectiveness of three optimization algorithms. Extensive simulation results show that PSO and IBPSO algorithms outperform that fine-tuned GA. More interestingly, the native PSO algorithm outperforms both the GA and the IBPSO algorithms in terms of solution speed and quality.

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