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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
DOIs
Publication statusPublished - 4 May 2017

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Cognitive radio
Engines
Neural networks
Intelligent agents
Radio systems
Quality of service
Communication systems
Communication

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@conference{3a73714a6310476d8e2fb8d06420cf13,
title = "Cognitive Radio Engine Learning Adaptation",
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.",
author = "Martins Olaleye and Keshav Dahal and Zeeshan Pervez",
year = "2017",
month = "5",
day = "4",
doi = "10.1109/SKIMA.2016.7916241",
language = "English",

}

Cognitive Radio Engine Learning Adaptation. / Olaleye, Martins; Dahal, Keshav; Pervez, Zeeshan.

2017.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Cognitive Radio Engine Learning Adaptation

AU - Olaleye, Martins

AU - Dahal, Keshav

AU - Pervez, Zeeshan

PY - 2017/5/4

Y1 - 2017/5/4

N2 - 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.

AB - 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.

U2 - 10.1109/SKIMA.2016.7916241

DO - 10.1109/SKIMA.2016.7916241

M3 - Paper

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