AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks †

Anirudh Warrier*, Saba Al-Rubaye, Gokhan Inalhan, Antonios Tsourdos

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Integrating 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.

Orijinal dilİngilizce
Makale numarası884
Basın numarası10
Yayın durumuYayınlandı - Eki 2023
Harici olarak yayınlandıEvet

Bibliyografik not

Publisher Copyright:
© 2023 by the authors.


This research was funded by Engineering and Physical Sciences Research Council (EPSRC) and Satellite Applications Catapult.

FinansörlerFinansör numarası
Satellite Applications Catapult
Engineering and Physical Sciences Research Council

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