  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).
