Throughput-Cost Dual-Objective Optimization for Multi-UAV Assisted WiFi Networks

Globecom, pp. 2523–2528

Abstract

In dense user scenarios, WiFi networks adopting IEEE 802.11n/ac standards often suffer from significant throughput degradation due to increased contention and frequent collisions. Unmanned aerial vehicles (UAVs), with their high mobility, on-demand deployment, and strong line-of-sight communication capabilities, provide a promising solution as supplementary communication infrastructure to offload users from overloaded WiFi access points. This paper investigates a multi-UAV assisted WiFi network architecture, aiming to maximize total network throughput while minimizing the number of deployed UAVs through the joint optimization of user association, UAV coordinates, and power allocation. To address the formulated NP-hard multi-objective optimization problem with dynamic dimensionality, we propose NSGA-II-HLA—a hybrid evolutionary algorithm that integrates a modified non-dominated sorting genetic algorithm II (NSGA-II) for global exploration with a distance-based heuristic for refined user association. Extensive simulation results demonstrate that the proposed approach significantly enhances network throughput, reduces UAV deployment cost, and achieves balanced performance across heterogeneous access domains.

Authors 4

1. Jingrui Liao

   Wuhan University

   Affiliation as printed

   Wuhan University,Electronic Information School,China

2. Yulin Hu

   Wuhan University

   Affiliation as printed

   Wuhan University,Electronic Information School,China

3. Jian Zhang

   Wuhan University

   Affiliation as printed

   Wuhan University,Electronic Information School,China

4. Anke Schmeink Aachen INDA Chair

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,INDA Chair,Germany