Interference mitigation for 5G-connected UAV using deep Q-learning framework

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dc.contributor.authorWarrier, Anirudh
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorPanagiotakopoulos, Dimitrios
dc.contributor.authorInalhan, Gokhan
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2022-11-04T15:26:25Z
dc.date.available2022-11-04T15:26:25Z
dc.date.issued2022-10-31
dc.description.abstractTo boost large-scale deployment of unmanned aerial vehicles (UAVs) in the future, a new wireless communication paradigm namely cellular-connected UAVs has recently received an upsurge of interest in both academia and industry. Fifth generation (5G) networks are expected to support this large-scale deployment with high reliability and low latency. Due to the high mobility, speed, and altitude of the UAVs there are numerous challenges that hinder its integration with the 5G architecture. Interference is one of the major roadblocks to ensuring the efficient co-existence between UAVs and terrestrial users in 5G networks. Conventional interference mitigation schemes for terrestrial networks are insufficient to deal with the more severe air-ground interference, which thus motivates this paper to propose a new algorithm to mitigate interference. A deep Q-learning (DQL) based algorithm is developed to mitigate interference intelligently through power control. The proposed algorithm formulates a non-convex optimization problem to maximize the Signal to Interference and Noise Ratio (SINR) and solves it using DQL. Its performance is measured as effective SINR against the complement cumulative distribution function. Further, it is compared with an adaptive link technique: Fixed Power Allocation (FPA), a standard power control scheme and tabular Q-learning(TQL). It is seen that the FPA has the worst performance while the TQL performs slightly better. This is since power control and interference coordination are introduced but not as effectively in the TQL method. It is observed that DQL algorithm outperforms the TQL implementation. To solve the severe air-ground interference experienced by the UAVs in 5G networks, this paper proposes a DQL algorithm. The algorithm effectively mitigates interference by optimizing SINR of the air-ground link and outperforms the existing methods. This paper therefore, proposes an effective algorithm to resolve the interference challenge in air-ground links for 5G-connected UAVs.en_UK
dc.identifier.citationWarrier A, Al-Rubaye S, Panagiotakopoulos D, et al., (2022) Interference mitigation for 5G-connected UAV using deep Q-learning framework. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), 18-22 September 2022, Portsmouth, Virginia, USAen_UK
dc.identifier.eisbn978-1-6654-8607-1
dc.identifier.eissn2155-7209
dc.identifier.isbn978-1-6654-8608-8
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/DASC55683.2022.9925817
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18656
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectFifth-generation(5G)en_UK
dc.subjectinterferenceen_UK
dc.subjectdeep Qlearningen_UK
dc.subjectunmanned aerial vehicles (UAVs)en_UK
dc.titleInterference mitigation for 5G-connected UAV using deep Q-learning frameworken_UK
dc.typeConference paperen_UK

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