AI-enabled interference mitigation for autonomous aerial vehicles in urban 5G networks

Simple item page
dc.contributor.authorWarrier, Anirudh
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorInalhan, Gokhan
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2023-10-20T11:20:11Z
dc.date.available2023-10-20T11:20:11Z
dc.date.issued2023-10-13
dc.description.abstractIntegrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel deep reinforcement learning algorithm, powered by AI, to address interference through power control. By formulating and solving a signal-to-interference-and-noise ratio (SINR) optimization problem using the deep Q-learning (DQL) algorithm, interference is effectively mitigated, and link performance is improved. Performance comparison with existing interference mitigation schemes, such as fixed power allocation (FPA), tabular Q-learning, particle swarm optimization, and game theory demonstrates the superiority of the DQL algorithm, where it outperforms the next best method by 41.66% and converges to an optimal solution faster. It is also observed that, at higher speeds, the UAV sees only a 10.52% decrease in performance, which means the algorithm is able to perform effectively at high speeds. The proposed solution effectively integrates UAVs with 5G networks, mitigates interference, and enhances link performance, offering a significant advancement in this field.en_UK
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC). Satellite Applications Catapulten_UK
dc.identifier.citationWarrier A, Al-Rubaye S, Inalhan G, Tsourdos A. (2023) AI-enabled interference mitigation for autonomous aerial vehicles in urban 5G networks, Aerospace, Volume 10, Issue 10, October 2023, Article Number 884en_UK
dc.identifier.issn2226-4310
dc.identifier.urihttps://doi.org/10.3390/aerospace10100884
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20419
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectautonomous vehiclesen_UK
dc.subjectunmanned aerial vehicles (UAVs)en_UK
dc.subjectfifth-generation (5G)en_UK
dc.subjectinterference mitigationen_UK
dc.subjectartificial intelligenceen_UK
dc.subjectdeep Q-learningen_UK
dc.titleAI-enabled interference mitigation for autonomous aerial vehicles in urban 5G networksen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Autonomous_aerial_vehicles_in_urban_5G_networks-2023.pdf
Size:
6.86 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)