상세정보
학술지
Deep Learning-Aided Downlink Beamforming Design and Uplink Power Allocation for UAV Wireless Communications with LoRa
- 발행일
- 202205
- 출처
- Applied Sciences, v.12 no.10, pp.1-14
- ISSN
- 2076-3417
- 출판사
- MDPI
- 협약과제
- 22HH4900, 저고도 소형드론 식별· 주파수 관리 기술 개발, 강규민
- 초록
- 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.
- KSP 제안 키워드
- Deep neural network(DNN), High accuracy, Linear Programming, Long-range, Multiple UAVs, Numerical results, Optimization techniques(OT), Second-order cone programming(SOCP), Wireless communication system, an unmanned aerial vehicle, base station(BS)
본 저작물은 크리에이티브 커먼즈 저작자 표시 (CC BY) 조건에 따라 이용할 수 있습니다.
