Machine Learning for Predictive Deployment of UAVs with Multiple Access
This work addresses efficient UAV deployment for cellular networks to reduce power consumption, but it is incremental as it builds on existing prediction and clustering techniques.
The paper tackles the problem of deploying UAVs as flying base stations to offload traffic from ground BSs by predicting future cellular traffic using an LSTM and determining service areas with a KEG algorithm, resulting in up to 24% reduction in total power consumption compared to conventional methods without prediction.
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).