Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers

Cognitive communication model perform the investigation and surveillance of spectrum in cognitive radio networks abetment in advertent primary users (PUs) and in turn help in allocation of transmission space for secondary users (SUs). In effective performance of regulation of wireless channel handov...

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Bibliographic Details
Published in:Cluster computing 2017-06, Vol.20 (2), p.1505-1515
Main Authors: Anandakumar, H., Umamaheswari, K.
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Artificial intelligence
Cognition & reasoning
Cognitive radio
Communication
Computation
Computer Communication Networks
Computer Science
Data communication
Decision making
Decisions
Machine learning
Operating Systems
Optimization techniques
Processor Architectures
Software
Spectrum allocation
Supervised learning
Wireless networks
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Summary:Cognitive communication model perform the investigation and surveillance of spectrum in cognitive radio networks abetment in advertent primary users (PUs) and in turn help in allocation of transmission space for secondary users (SUs). In effective performance of regulation of wireless channel handover strategy in cognitive computing systems, new computing models are desired in operating set of tasks to process business model, and interact naturally with humans or machine rather being programmed. Cognitive wireless network are trained via artificial intelligence (AI) and machine learning (ML) algorithms for dynamic processing of spectrum handovers. They assist human experts in making enhanced decisions by penetrating into the complexity of the handovers. This paper focuses on learning and reasoning features of cognitive radio (CR) by analyzing primary user (PU) and secondary user (SU) data communication using home location register (HLR) and visitor location register (VLR) database respectively. The SpecPSO is proposed for optimizing handovers using supervised machine learning technique for performing dynamic handover by adapting to the environment and make smart decisions compared to the traditional cooperative spectrum sensing (CSS) techniques.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-017-0798-3