Machine Learning for Predictive Deployment of UAVs with Multiple Access

• URL: http://arxiv.org/abs/2003.02631v2
• Date: Thu, 30 Jul 2020 13:21:21 GMT
• Title: Machine Learning for Predictive Deployment of UAVs with Multiple Access
• Authors: Linyan Lu and Zhaohui Yang and Mingzhe Chen and Zelin Zang and Mohammad Shikh-Bahaei
• Abstract summary: In this paper, a machine learning deployment framework of unmanned aerial vehicles (UAVs) is studied. Due to time-varying traffic distribution, a long short-term memory (LSTM) based prediction is introduced to predict the future cellular traffic. The proposed method can reduce up to 24% of the total power consumption compared to the conventional method.
• Score: 37.49465317156625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied. In the considered model, UAVs are deployed as flying base stations (BS) to offload heavy traffic from ground BSs. Due to time-varying traffic distribution, a long short-term memory (LSTM) based prediction algorithm is introduced to predict the future cellular traffic. To predict the user service distribution, a KEG algorithm, which is a joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture model (GMM), is proposed for determining the service area of each UAV. Based on the predicted traffic, the optimal UAV positions are derived and three multi-access techniques are compared so as to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24\% of the total power consumption compared to the conventional method without traffic prediction. Besides, rate splitting multiple access (RSMA) has the lower required transmit power compared to frequency domain multiple access (FDMA) and time domain multiple access (TDMA).

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