Experience level analysis for a cognitive radio engine

Hamed Asadi, Haris Volos, Michael Mahmoud Marefat, Tamal Bose

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A cognitive radio engine (CE) is where the advanced adaptation algorithms for a cognitive radio is implemented. A CE is an intelligent agent which observes the radio environment and chooses the best communication settings that meet the application’s goal. In this process, providing reliable performance is one of the major challenges faced by a CE. Therefore, one of the most important issues in designing CEs is the ability to characterize and reliably predict performance of the CE in different operating scenarios. An operating scenario is defined as the set of the operating objective, channel availability, and channel quality metrics. In this paper, we develop several performance evaluation and prediction indices to quantify the amount of knowledge of different CE algorithms independently of the implementation approach and/or their operating scenarios. Using these new indices, we are able to provide a more accurate estimation of the learning process and future performance of each individual CE algorithm. A number of simulation-based experiments was conducted. Our results show that proposed contextual CE algorithms based on the developed knowledge indicators is able to improve the wireless communication system’s objective rewards significantly. In effect, the contextual CE is able to deliver about 10% more data than the CE with the fixed exploration rate.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalAnalog Integrated Circuits and Signal Processing
DOIs
StateAccepted/In press - May 4 2018

Fingerprint

Cognitive radio
Engines
Intelligent agents
Communication systems
Availability
Communication

Keywords

  • Cognitive engine
  • Cognitive radio
  • Experience level
  • Knowledge indicator

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Surfaces, Coatings and Films

Cite this

Experience level analysis for a cognitive radio engine. / Asadi, Hamed; Volos, Haris; Marefat, Michael Mahmoud; Bose, Tamal.

In: Analog Integrated Circuits and Signal Processing, 04.05.2018, p. 1-12.

Research output: Contribution to journalArticle

@article{89baae023b6e43e8973b5af128a8dcce,
title = "Experience level analysis for a cognitive radio engine",
abstract = "A cognitive radio engine (CE) is where the advanced adaptation algorithms for a cognitive radio is implemented. A CE is an intelligent agent which observes the radio environment and chooses the best communication settings that meet the application’s goal. In this process, providing reliable performance is one of the major challenges faced by a CE. Therefore, one of the most important issues in designing CEs is the ability to characterize and reliably predict performance of the CE in different operating scenarios. An operating scenario is defined as the set of the operating objective, channel availability, and channel quality metrics. In this paper, we develop several performance evaluation and prediction indices to quantify the amount of knowledge of different CE algorithms independently of the implementation approach and/or their operating scenarios. Using these new indices, we are able to provide a more accurate estimation of the learning process and future performance of each individual CE algorithm. A number of simulation-based experiments was conducted. Our results show that proposed contextual CE algorithms based on the developed knowledge indicators is able to improve the wireless communication system’s objective rewards significantly. In effect, the contextual CE is able to deliver about 10{\%} more data than the CE with the fixed exploration rate.",
keywords = "Cognitive engine, Cognitive radio, Experience level, Knowledge indicator",
author = "Hamed Asadi and Haris Volos and Marefat, {Michael Mahmoud} and Tamal Bose",
year = "2018",
month = "5",
day = "4",
doi = "10.1007/s10470-018-1199-0",
language = "English (US)",
pages = "1--12",
journal = "Analog Integrated Circuits and Signal Processing",
issn = "0925-1030",
publisher = "Springer Netherlands",

}

TY - JOUR

T1 - Experience level analysis for a cognitive radio engine

AU - Asadi, Hamed

AU - Volos, Haris

AU - Marefat, Michael Mahmoud

AU - Bose, Tamal

PY - 2018/5/4

Y1 - 2018/5/4

N2 - A cognitive radio engine (CE) is where the advanced adaptation algorithms for a cognitive radio is implemented. A CE is an intelligent agent which observes the radio environment and chooses the best communication settings that meet the application’s goal. In this process, providing reliable performance is one of the major challenges faced by a CE. Therefore, one of the most important issues in designing CEs is the ability to characterize and reliably predict performance of the CE in different operating scenarios. An operating scenario is defined as the set of the operating objective, channel availability, and channel quality metrics. In this paper, we develop several performance evaluation and prediction indices to quantify the amount of knowledge of different CE algorithms independently of the implementation approach and/or their operating scenarios. Using these new indices, we are able to provide a more accurate estimation of the learning process and future performance of each individual CE algorithm. A number of simulation-based experiments was conducted. Our results show that proposed contextual CE algorithms based on the developed knowledge indicators is able to improve the wireless communication system’s objective rewards significantly. In effect, the contextual CE is able to deliver about 10% more data than the CE with the fixed exploration rate.

AB - A cognitive radio engine (CE) is where the advanced adaptation algorithms for a cognitive radio is implemented. A CE is an intelligent agent which observes the radio environment and chooses the best communication settings that meet the application’s goal. In this process, providing reliable performance is one of the major challenges faced by a CE. Therefore, one of the most important issues in designing CEs is the ability to characterize and reliably predict performance of the CE in different operating scenarios. An operating scenario is defined as the set of the operating objective, channel availability, and channel quality metrics. In this paper, we develop several performance evaluation and prediction indices to quantify the amount of knowledge of different CE algorithms independently of the implementation approach and/or their operating scenarios. Using these new indices, we are able to provide a more accurate estimation of the learning process and future performance of each individual CE algorithm. A number of simulation-based experiments was conducted. Our results show that proposed contextual CE algorithms based on the developed knowledge indicators is able to improve the wireless communication system’s objective rewards significantly. In effect, the contextual CE is able to deliver about 10% more data than the CE with the fixed exploration rate.

KW - Cognitive engine

KW - Cognitive radio

KW - Experience level

KW - Knowledge indicator

UR - http://www.scopus.com/inward/record.url?scp=85046435535&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046435535&partnerID=8YFLogxK

U2 - 10.1007/s10470-018-1199-0

DO - 10.1007/s10470-018-1199-0

M3 - Article

AN - SCOPUS:85046435535

SP - 1

EP - 12

JO - Analog Integrated Circuits and Signal Processing

JF - Analog Integrated Circuits and Signal Processing

SN - 0925-1030

ER -