Ajou University repository

Unmanned Aerial Vehicle Based Adaptive Spectrum Sensing for Cognitive Radio Systems
  • Gul, Noor ;
  • Been, Gyoungmin ;
  • Kim, Su Min ;
  • Ali, Jehad ;
  • Kim, Junsu
Citations

SCOPUS

0

Citation Export

Publication Year
2024-01-01
Journal
2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024
Keyword
cognitive radioreinforcement learningSpectrum sensingunmanned aerial vehiclesvirtual cooperative sensing
Mesh Keyword
Aerial vehicleCooperative sensingLine of sight communicationsNetwork throughputReinforcement learningsSecondary userSensing schemesSpectrum sensingUnmanned aerial vehicleVirtual cooperative sensing
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsSafety, Risk, Reliability and QualityControl and OptimizationInstrumentation
Abstract
Spectrum sensing utilizing unmanned aerial vehicles (UAVs) has become increasingly popular due to their advantageous line of sight (LoS) communication links. In this study, we propose and evaluate an adaptive spectrum sensing scheme with reinforcement learning for UAV based cognitive radio systems. Instead of employing multiple secondary users (SUs) as in a terrestrial cooperative spectrum sensing setup with a fusion center (FC), our approach involves a single UAV performing virtual cooperative sensing by flying on a circular aerial trajectory. The UAV's sensing period consists of virtual mini-sensing slots akin to a group of SUs. The UAV enhances sensing reliability by performing local spectrum sensing within each mini-slot and combines the collected data using the voting scheme to make collective decisions. Moreover, traditional UAV-based virtual cooperative sensing schemes are facing problems in adjusting the UAV velocity, radius, and flight height to get maximum network throughput. Therefore, these variables are manually tuned for the UAV and are kept fixed. However, in the proposed scheme UAV follows reinforcement learning to adjust the UAV parameters by increasing and decreasing their levels intelligently to obtain higher rewards as the network throughput.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37118
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85206111710&origin=inward
DOI
https://doi.org/10.1109/apwcs61586.2024.10679293
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10679110
Type
Conference
Funding
This research outcome is helped in part by the National Research Foundation of Korea (NRF) grants financed by the Korea government (MSIT) (No. 2021R1A2C1013150 and 2022R1F1A1074556).
Show full item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

ALI JEHAD Image
ALI JEHADJEHAD, ALI
Department of Software and Computer Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.