Cognitive Radio Engine Learning Adaptation

Martins Olaleye, Keshav Dahal, Zeeshan Pervez

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)
249 Downloads (Pure)

Abstract

Cognitive radio (CR) system being an intelligence-based communication device has been considered as the next generation emerging technologies to Wireless Communication Systems (WCS). This CR's embedded-intelligent agent is called Cognitive Engine (CE), and is responsible for the dynamic adaptation between the WCS's environment and the radio operational parameters. As a result of CR's intelligence capability, the WCS's quality of service (QoS) and its connectivity operations get enhanced. In order to evaluate the CR engine performance in respect to its learning, timing, and its computational performances. This paper proposes an alternative state-of-the-art enhanced CR learning engine based on Random Neural Network (RNN). Unlike Artificial Neural Network (ANN) systems, RNN establishes strong data generalization, converges faster and produces relatively smaller levels of prediction errors. Subjected to similar environmental conditions, the simulation cumulative results show that the performance of the proposed RNN system is satisfactory and produces 36.895% performance improvement above the ANN learning engine.
Original languageEnglish
Pages325-332
Number of pages8
DOIs
Publication statusPublished - 4 May 2017
Event2016 10th International conference on software; knowledge, information management & applications - Chengdu, China
Duration: 15 Dec 201617 Dec 2016

Conference

Conference2016 10th International conference on software; knowledge, information management & applications
Abbreviated titleSKIMA
Country/TerritoryChina
CityChengdu
Period15/12/1617/12/16

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