Fast Online Movement Optimization of Aerial Base Stations Based on Global Connectivity Map

TL;DR

The paper addresses the NP-hard problem of moving multiple aerial base stations (ABSs) to maximize the mean coverage rate for mobile ground users (GUs) in site-specific environments with blockages. It introduces a Global Connectivity Map (GCM) to convert the ABS movement problem into a set of binary integer linear programs (BILPs) and solves each subproblem with a fast online algorithm based on projected stochastic subgradient descent in the dual space, leveraging a three-level time hierarchy. The approach achieves high coverage performance close to the SCIP upper bound while substantially reducing computation time, and it outperforms regression-based deep reinforcement learning and K-means–initiated evolutionary algorithms in both coverage and efficiency. This enables real-time, mobility-aware ABS placement in complex urban-like environments, with demonstrated robustness to dynamic GU scenarios and configurable grid resolutions.

Abstract

Aerial base stations (ABSs) mounted on unmanned aerial vehicles (UAVs) are capable of extending wireless connectivity to ground users (GUs) across a variety of 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. The complexity of the problem escalates in environments where the signal propagation is obstructed by localized obstacles such as buildings, and is further compounded by the dynamic GU positions. In response to these challenges, this paper focuses on the optimization of a multi-ABS movement problem, aiming to improve the mean CR for mobile GUs within a site-specific environment. Our proposals include 1) introducing the concept of 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 programming (BILP) problem based on GCM; 3) and 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 method achieves a high CR performance close to the upper bound obtained by the open-source solver (SCIP), yet with significantly reduced running time. Moreover, our method also outperforms common benchmarks in the literature such as the K-means initiated evolutionary algorithm or the ones based on deep reinforcement learning (DRL), in terms of CR performance and/or time efficiency.

Figures (7)

• Figure 1: Site-specific ABS movement to cover mobile GUs.
• Figure 2: Discretized ABS/GU plane.
• Figure 3: Trial-Period-Step time hierarchy.
• Figure 4: Illustration of ABS flight trajectory during consecutive steps ($N=2$, $M=20$). The squares represent the generated BBs, whose color darkness indicates their heights. The initial ABSs is denoted by blue points, and the final ABSs is denoted by magenta points. The hollow points represent the positions of ABSs during the flight period. Triangles symbolize GUs, with green and red coloring indicating covered and uncovered GUs, respectively.
• Figure 5: Average coverage rate of the four different algorithms with $400$ steps over 5 different trials($N=2$, $M=20$).