Fast Online Movement Optimization of Aerial Base Stations Based on Global Connectivity Map

Unmanned aerial vehicles (UAVs) can serve as aerial base stations (ABSs) to provide wireless connectivity for ground users (GUs) in diverse scenarios. However, it is an NP-hard problem with exponential complexity in $M$ and $N$, in order to maximize the coverage rate (CR) of $M$ GUs by jointly placing $N$ ABSs with limited coverage range. This problem becomes even more intricate when the coverage range becomes irregular due to site-specific obstructions (e.g., buildings) on the air-ground channel, and/or when the GUs are in motion. To address the above challenges, we study a multi-ABS movement optimization problem to maximize the average coverage rate of mobile GUs within a site-specific environment. We tackle this challenging problem by 1) constructing the global connectivity map (GCM) which contains the connectivity information between given pairs of ABS/GU locations; 2) partitioning the ABS movement problem into ABS placement sub-problems and formulate each sub-problem into a binary integer linear programing (BILP) problem based on GCM; 3) proposing a fast online algorithm to execute (one-pass) projected stochastic subgradient descent within the dual space to rapidly solve the BILP problem with near-optimal performance. Numerical results demonstrate that our proposed algorithm achieves a high CR performance close to that obtained by the open source solver (SCIP), yet with significantly reduced running time. In addition, the algorithm also notably outperforms one of the state-of-the-art deep reinforcement learning (DRL) methods and the K-means initiated evolutionary algorithm in terms of CR performance and/or time efficiency.