A novel learning-based spectrum sensing technique for cognitive radio networks

Aydin, Mehmet Emin, Safdar, Ghazanfar and Aslam, Nauman (2013) A novel learning-based spectrum sensing technique for cognitive radio networks. In: WAINA 2013 - 27th International Conference on Advanced Information Networking and Applications Workshops, 25th - 28th March 2013, Barcelona, Spain.

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Abstract

Spectrum sensing is one of the most challenging issues in Cognitive Radio (CR) networks. It should be performed efficiently to reduce number of false alarms and missed detections. This paper presents a novel approach, which employs collective intelligence developed via learning agents, for spectrum sensing in CR networks. The approach is used to share the sensed information, then digest it and make intelligent decisions about the presence or absence of primary users (PUs), by exploiting the accumulated history. The usage of history thus results in reduced sensing, subsequently requiring minimum activity in the common control channel (CCC), to help secondary users (SUs) exchange information and switch to the chosen empty space(s). Paper provides implementation of the proposed approach based on maxminfunctions integrated with a probabilistic decision making process. The performance analysis of the proposed approach shows that the usage of accumulated history by CR nodes results in reduced spectrum sensing by fine tuning the scan threshold.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	History; Learning Agents; Scan Threshold; Spectrum Sensing
Subjects:	G400 Computer Science G700 Artificial Intelligence
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	16 Jan 2015 14:53
Last Modified:	13 Oct 2019 00:33
URI:	http://nrl.northumbria.ac.uk/id/eprint/20816

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