Spatial Deep Learning for Site-Specific Movement Optimization of Aerial
  Base Stations
Jiangbin Lyu Xu Chen Jiefeng Zhang Liqun Fu
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.