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.

翻译：无人机（UAV）可作为空中基站（ABS）在各种应急场景中为地面用户（GU）提供无线连接。然而，通过联合配置覆盖范围有限的N个空中基站以最大化M个地面用户的覆盖率，是一个在M和N上具有指数复杂度的NP-hard问题。当覆盖范围因站点特定阻塞（如建筑物）在空地信道上变得不规则，和/或地面用户发生移动时，问题进一步复杂化。为应对上述挑战，我们研究了多空中基站移动优化问题，以在站点特定环境中最大化移动地面用户的平均覆盖率。本文提出基于多维表型精英档案的空间深度学习（SDL-ME）算法来解决这一难题，其方法包括：1）将复杂的空中基站移动问题分解为多个跨有限时间窗的空中基站部署子问题；2）使用编码器-解码器深度神经网络（DNN）作为模拟器，捕获空中基站/地面用户的空间相关性，从而降低与实际环境交互的成本；3）利用该模拟器加速质量多样性搜索以获取最优部署方案；4）提出规划-探索-服务机制用于多空中基站移动协调。数值结果表明，所提方法在平均覆盖率、训练时间和/或样本效率上显著优于基准深度强化学习（DRL）方法及其他两种基线。此外，经过一次性训练后，所提方法可应用于空中基站/地面用户数量在站点动态变化和/或地面用户速度不同/变化的场景，因此相比传统基于DRL的方法具有更强的鲁棒性和灵活性。