Proactive Power Control and Position Deployment for Drone Small Cells: Joint Supervised and Unsupervised Learning

• Main Authors: Shao-Hung Cheng, Yen-Ting Shih, Ko-Chin Chang
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Data driven; supervised learning; unsupervised learning; mobility prediction; drone small cells
• Online Access: https://ieeexplore.ieee.org/document/9535513/

Since unmanned aerial vehicles (UAV) are easily deployed, highly mobile, and hover capability, they are utilized for many commercial applications. In particular, small cells mounted on UAVs, also known as drone small cells (DSC), may provide temporary relief or ancillary programs for the wireless network. In this paper, we design a prediction-based proactive drone management (P²DM) framework to reduce network interference and improve energy efficiency in the multiple DSCs scenario. The P²DM framework can be divided into offline and online phases. In the offline phase, supervised learning is used to build a highly accurate mobility prediction model according to the historical data. The prediction model is launched in the online phase to predict the user position only using a small sample set. The system proactively determines whether a DSC should be awake or asleep at the next timeslot due to the predicted user positions. Since DSC has more longer awake time in the deep sleeping mode, it is previously awoken to avoid data propagation delay. To further overcome the difficulty of obtaining the key performance indicator data (i.e., labeled data) in the online phase, an unsupervised learning technique is employed for DSC repositioning and power control to improve energy efficiency. Our simulation results show that the P²DM framework can demonstrate the advantage in terms of execution time and energy efficiency compared to the existing method based on genetic algorithm (i.e., a heuristic algorithm).