Poster: Optimizing ABS Deployment via LSTM-based Mobility and Data Traffic Demand Prediction

This paper proposes a proactive aerial base station (ABS) deployment framework for hotspots that optimizes the placement of ABSs based on mobility and data traffic demand prediction using a LSTM model. We design a greedy-based ABS deployment algorithm to solve the data rate maximization problem, which is known to be NP-hard. Simulation results demonstrate that the proposed algorithm achieves a higher data rate with fewer ABSs compared to benchmark methods.