  Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations
(ABSs) to provide wireless connectivity for ground users (GUs) in various
emergency scenarios. However, it is a NP-hard problem with exponential
complexity in $M$ and $N$, in order to maximize the coverage rate of $M$ GUs by
jointly placing $N$ ABSs with limited coverage range. The problem is further
complicated when the coverage range becomes irregular due to site-specific
blockages (e.g., buildings) on the air-ground channel, and/or when the GUs are
moving. To address the above challenges, we study a multi-ABS movement
optimization problem to maximize the average coverage rate of mobile GUs in a
site-specific environment. The Spatial Deep Learning with Multi-dimensional
Archive of Phenotypic Elites (SDL-ME) algorithm is proposed to tackle this
challenging problem by 1) partitioning the complicated ABS movement problem
into ABS placement sub-problems each spanning finite time horizon; 2) using an
encoder-decoder deep neural network (DNN) as the emulator to capture the
spatial correlation of ABSs/GUs and thereby reducing the cost of interaction
with the actual environment; 3) employing the emulator to speed up a
quality-diversity search for the optimal placement solution; and 4) proposing a
planning-exploration-serving scheme for multi-ABS movement coordination.
Numerical results demonstrate that the proposed approach significantly
outperforms the benchmark Deep Reinforcement Learning (DRL)-based method and
other two baselines in terms of average coverage rate, training time and/or
sample efficiency. Moreover, with one-time training, our proposed method can be
applied in scenarios where the number of ABSs/GUs dynamically changes on site
and/or with different/varying GU speeds, which is thus more robust and flexible
compared with conventional DRL-based methods.
