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Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

Arshad, Kamran, Imran, Muhammad Ali and Moessner, Klaus (2010) Collaborative spectrum sensing optimisation algorithms for cognitive radio networks. International Journal of Digital Multimedia Broadcasting, 2010:424036. pp. 1-20. ISSN 1687-7578 (Print), 1687-7586 (Online) (doi:10.1155/2010/424036)

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Abstract

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance.

Item Type: Article
Additional Information: [1] Article ID 424036. [2] This work was undertaken within project E3 which received research funding from the EU FP7 framework. This paper reflects only the authors views and the community is not liable for any use that may be made of the information contained therein. The contributions of colleagues from the E3 consortium are hereby acknowledged. [3] Copyright © 2010 Kamran Arshad et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: cognitive radio networks, algorithms
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Pre-2014 Departments: School of Engineering
School of Engineering > Mobile & Wireless Communications Research Laboratory
Related URLs:
Last Modified: 15 Oct 2016 20:39
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/9373

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