Fast Online Movement Optimization of Aerial Base Stations Based on Global Connectivity Map
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要