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

2024-05-04

arXiv.org

AI Summary

This paper addresses the real-time deployment of multiple unmanned aerial base stations (UABSs) in dynamic urban environments to maximize the average connectivity ratio (CR) of mobile ground users (GUs), under challenges posed by obstacle occlusion and time-varying GU locations. We propose a Global Connectivity Map (GCM) to model the spatial connectivity relationships between UABSs and GUs, decomposing the mobility optimization into a series of binary integer linear programming (BILP) subproblems grounded in the GCM. An online one-shot projection stochastic subgradient algorithm is then designed in the dual space for efficient solution. Compared to the SCIP solver, our method achieves CR performance close to the theoretical upper bound while drastically reducing computation time. Against K-means plus evolutionary algorithms and deep reinforcement learning baselines, it delivers significant improvements in both CR and runtime efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimize aerial base station movement to enhance user coverage
• Address NP-hard complexity in dynamic, obstacle-rich environments
• Develop fast online algorithm for near-optimal placement efficiency

Innovation

• Uses global connectivity map for ABS/GU links
• Partitions movement into BILP placement sub-problems
• Solves BILP via fast online dual-space algorithm