Channel assignment based on bee algorithms in multi‐hop cognitive radio networks. Issue 13 (1st September 2014)

• Record Type: Journal Article
• Authors: Ghasemi, Ahmad; Masnadi‐Shirazi, Mohammad Ali; Biguesh, M.; Qassemi, Foad
• Is Part Of: IET communications. Volume 8:Issue 13(2014)
• Page Start: 2356
• Page End: 2365
• Publication Date: 2014-09-01
• Subjects: cognitive radio -- evolutionary computation -- matrix algebra -- channel allocation; channel assignment matrix -- multihop cognitive radio networks -- spectrum management policies -- dynamic spectrum management -- DSM -- spectrum utilisation -- CR -- open spectrum systems -- artificial bee colony algorithm -- ABC algorithm -- bee swarm optimisation algorithm -- BSO algorithm -- evolutionary algorithms -- colour sensitive graph colouring -- CSGC -- channel availability -- interference constraints; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2013.0910
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : Spectrum management policies are responsible for poor utilisation of the radio spectrum. By carrying out dynamic spectrum management (DSM), cognitive radio (CR) can increase the radio spectrum in wireless systems efficiently. CR technology accounts for the improvement in the spectrum utilisation significantly. One issue of DSM in CR is the assignment of frequency channels among its users. Herein, a general model and four utility functions for optimal channel assignment in open spectrum systems such as CR networks have been defined. First, a new utility function with a better fairness than the other functions is proposed. Then, two new different channel assignment methods, based on the artificial bee colony (ABC) and bee swarm optimisation (BSO) algorithms, are proposed, whereas other certain evolutionary algorithms and colour sensitive graph colouring (CSGC) are used to compare the performances. In order to decrease the search space, based on the channel availability and interference constraints a mapping process between the channel assignment matrix and the position of the bees has been proposed. Our simulation results, compared to the optimal solutions, show that our algorithms drastically improve network performance by reducing interference.