NDLI: Learning-based predictive dynamic spectrum access framework: a practical perspective for enhanced QoE of secondary users

Content Provider	IET Digital Library
Author	Agarwal, Anirudh Gangopadhyay, Ranjan Dubey, Shivangi Debnath, Soumitra Khan, Mohd Asif
Abstract	In a cognitive radio environment, the optimality in channel selection by a secondary user (SU) is directly dependent on its spectrum-sensing efficiency, and quality of experience (QoE) in terms of the channel-switching frequency (CSF) and the interference caused to the primary users (PUs). Modelling the spectrum through statistical methods becomes, sometimes, difficult due to the lack of a-priori information of the PU activity. This work proposes a framework for learning-based prediction of the future idle times of the PUs thereby opportunistically allocating the channel with enhanced QoE of SUs. The idea is to minimise the spectrum-sensing energy requirement by sensing only if the channel is predicted to be idle, thereby reducing the CSF and mitigating the SU–PU interference. Initially, the authors have tested the accuracy of the prediction approach in various traffic scenarios for a single PU channel case. Later, it is extended to the multiple channel case for a particular data traffic. Furthermore, a practical scenario has been considered where the efficacy of the proposed framework is validated for PU data traffic in GSM and ISM bands. The results highlight the practicability of prediction-based opportunistic dynamic spectrum access with improvement in the SU QoE over random channel selection.
Starting Page	2243
Ending Page	2252
Page Count	10
ISSN	17518628
Volume Number	12
e-ISSN	17518636
Issue Number	Issue 18, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/12/18
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2018.5407
Journal	IET Communications
Publisher Date	2018-07-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Apriori Information Channel-switching Frequency Cognitive Radio Cognitive Radio Environment CSF Electromagnetic Compatibility Enhanced QoE Future Idle Times Interference Learning in AI Learning-based Prediction Multiple Channel Case Particular Data Traffic Practicability Practical Perspective Practical Scenario Prediction Approach Prediction-based Opportunistic Dynamic Spectrum Access Predictive Dynamic Spectrum Access Framework Primary User PU Activity PU Data Traffic PUs Radio Link And Equipment Radio Spectrum Management Random Channel Selection Secondary User Single PU Channel Case Spectrum-sensing Efficiency Spectrum-sensing Energy Requirement Statistical Method Statistics SU QoE SU–PU Interference Traffic Scenario
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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