Advanced search ?

Object

Download

Title: Evaluation of Radio Channel Utility using Epsilon-Greedy Action Selection, Journal of Telecommunications and Information Technology, 2021, nr 3

Creator:

Malon, Krzysztof

Date:

2021, nr 3

Resource Type:

artykuł

Description:

This paper presents an algorithm that supports the dynamic spectrum access process in cognitive radio networks by generating a sorted list of best radio channels or by identifying those frequency ranges that are not in use temporarily. The concept is based on the reinforcement learning technique named Q-learning. To evaluate the utility of individual radio channels, spectrum monitoring is performed. In the presented solution, the epsilon-greedy action selection method is used to indicate which channel should be monitored next. The article includes a description of the proposed algorithm, scenarios, metrics, and simulation results showing the correct operation of the approach relied upon to evaluate the utility of radio channels and the epsilon-greedy action selection method. Based on the performed tests, it is possible to determine algorithm parameters that should be used in this proposed deployment. The paper also presents a comparison of the results with two other action selection methods

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852 ; oai:bc.itl.waw.pl:2191

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności

Subject and keywords:

cognitive radio machine learning dynamic spectrum access spectrum monitoring Q-learning

Object collections:

Last modified:

Oct 11, 2021

In our library since:

Oct 11, 2021

Number of object content hits:

43

All available object's versions:

https://bc.itl.waw.pl/publication/2493

Show description in RDF format:

RDF

Show description in OAI-PMH format:

OAI-PMH

Edition name Date
Malon, Krzysztof, 2021, nr 3, Evaluation of Radio Channel Utility using Epsilon-Greedy Action Selection, Journal of Telecommunications and Information Technology, 2021, nr 3 Oct 11, 2021

Objects Similar
Markov Decision Process based Model for Performance Analysis an Intrusion Detection System in IoT Networks, Journal of Telecommunications and Information Technology, 2021, nr 3

Kalnoor, Gauri S, Gowrishankar

2021, nr 3
artykuł
Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods, Journal of Telecommunications and Information Technology, 2020, nr 3

Khamayseh, Sundous Halawani, Alaa

2020, nr 3
artykuł
2024, nr 3, JTIT-artykuły
2024, nr 3, JTIT-artykuły
A Generalized Learning Approach to Deep Neural Networks, Journal of Telecommunications and Information Technology, 2024, nr 3

Ponti, Francesca Frezza, Fabrizio Simeoni, Patrizio Parisi, Raffaele

2024, nr 3
artykuł
Optimal Voting Rule and Minimization of Total Error Rate in Cooperative Spectrum Sensing for Cognitive Radio Networks, Journal of Telecommunications and Information Technology, 2021, nr 1

Bachan, P. Trankatwar, Sachin Ravikant Ghosh, Samit Kumar

2021, nr 1
artykuł
Implementation of Selected Spectrum Sensing Systems for Cognitive Radio Networks using FPGA Platform, Journal of Telecommunications and Information Technology, 2018, nr 4

Abdullah, Hikmat N. Abed, Hadeel S.

2018, nr 4
artykuł
Mathematical Foundations of Cognitive Radios, Journal of Telecommunications and Information Technology, 2009, nr 4

Couillet, Romain Debbah, Merouane

2009, nr 4
artykuł
Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning, Journal of Telecommunications and Information Technology, 2022, nr 1

Fauzi, Ahmad Hadinata Iskandar, D. N. F. Awang Ganum, Adriana Chin, Lim Phei

2022, nr 1
artykuł
Network Traffic Classification in an NFV Environment using Supervised ML Algorithms, Journal of Telecommunications and Information Technology, 2021, nr 3

Latkoski, Pero Ilievski, Gjorgji

2021, nr 3
artykuł
More
×

Citation

Citation style:

Contact details

Address

Instytut Łączności – Państwowy Instytut Badawczy ul. Szachowa 1 04-894 Warszawa

Phone

(+48) 5128 696

E-Mail

biblioteka@il-pib.pl

Visit us!

This page uses 'cookies'. More information