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.

无人机可以作为空中基站在不同的紧急场景中为地面用户提供无线连接。本文提出了一种空间深度学习与多维精英表征存档（SDL-ME）算法，用于解决联合放置受限覆盖范围的多个空中基站，以最大化移动用户的平均覆盖率。算法通过将复杂的基站移动问题划分为多个子问题，并利用编码 - 解码深度神经网络（DNN）作为仿真器来捕捉基站与用户的空间相关性，从而减少与实际环境的交互成本，并提供了用于多个基站移动协调的规划 - 探索 - 服务方案。数值结果表明，所提出的方法在平均覆盖率、训练时间和 / 或样本效率方面显著优于基准深度强化学习（DRL）方法和其他两种基准方法。此外，所提出的方法在一次训练后可以应用于基站数目动态变化和 / 或具有不同 / 变化用户速度的场景，相对于传统的 DRL 方法更为鲁棒和灵活。