Sub-Connected Hybrid Precoding and Trajectory Optimization Using Deep Reinforcement Learning for Energy-Efficient Millimeter-Wave UAV Communications
- Authors
- Silvirianti, Soo Young; Shin, Soo Young
- Issue Date
- Sep-2023
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Precoding; Autonomous aerial vehicles; Radio frequency; Energy efficiency; Trajectory; Optimization; Antennas; Deep reinforcement learning; energy efficiency; hybrid precoding; mmWave; sub-connected; UAV
- Citation
- IEEE WIRELESS COMMUNICATIONS LETTERS, v.12, no.9, pp.1642 - 1646
- Journal Title
- IEEE WIRELESS COMMUNICATIONS LETTERS
- Volume
- 12
- Number
- 9
- Start Page
- 1642
- End Page
- 1646
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21781
- DOI
- 10.1109/LWC.2023.3286110
- ISSN
- 2162-2337
- Abstract
- In this letter, a sub-connected hybrid precoding system was designed to realize energy-efficient millimeter-Wave (mmWave) unmanned aerial vehicle (UAV) communications. Considering the limited capacity of the UAV battery, the system was jointly optimized with a UAV trajectory to increase the energy efficiency of the UAV under quality-of-service (QoS) and power budget constraints. The dynamic motion of the UAV changes the channel condition between the UAV and terrestrial users over time. Hence, a joint optimization problem was formulated as a non-convex and time-sequential domain, solved using deep reinforcement learning (DRL). The performances of the proposed scheme and a fully-connected hybrid precoding scheme were compared in terms of energy efficiency and show higher results.
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