Deep Learning-Aided Downlink Beamforming Design and Uplink Power Allocation for UAV Wireless Communications with LoRa

Applied Sciences

Jun-Hyun Park¹  Jae-Mo Kang¹  Yeong-Rok Kim¹  Kyu-Min Kang²  Dong-Woo Lim² 

[1] Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea;Radio & Satellite Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea;

关键词: beamforming design;    convex optimization;    deep learning;    LoRa (long range);    UAV (unmmaned aerial vehicle);   

DOI  :  10.3390/app12104826

来源: DOAJ

【 摘 要 】

In this paper, we consider an unmanned aerial vehicle (UAV) wireless communication system where a base station (BS) equipped multi antennas communicates with multiple UAVs, each equipped with a single antenna, using the LoRa (Long Range) modulation. The traditional approaches for downlink beamforming design or uplink power allocation rely on the convex optimization technique, which is prohibitive in practice or even infeasible for the UAVs with limited computing capabilities, because the corresponding convex optimization problems (such as second-order cone programming (SOCP) and linear programming (LP)) requiring a non-negligible complexity need to be re-solved many times while the UAVs move. To address this issue, we propose novel schemes for beamforming design for downlink transmission from the BS to the UAVs and power allocation for uplink transmission from the UAVs to the BS, respectively, based on deep learning. Numerical results demonstrate a constructed deep neural network (DNN) can predict the optimal value of the downlink beamforming or the uplink power allocation with low complexity and high accuracy.

【 授权许可】

Unknown