Multi-Agent Deep Reinforcement Learning for Optimized Multi-UAV Coverage and Power-Efficient UE Connectivity

2025-03-30

AI Summary

In emergency scenarios such as post-disaster response and battlefield communications, multi-UAV networks face critical challenges in achieving collaborative coverage while suppressing inter-cluster interference. Method: This paper proposes an end-to-end joint optimization framework that maximizes the number of served user equipment (UE) under minimum data-rate constraints while minimizing cross-cluster interference. For the first time, we integrate multi-agent deep deterministic policy gradient (MADDPG) with K-means-based UE clustering and 3D line-of-sight/non-line-of-sight (LOS/NLOS) channel modeling to jointly optimize UAV 3D deployment and downlink power allocation. Results: Experiments demonstrate that our approach improves UE coverage ratio by 2.07× and 8.84× over DQN-based and equal-power allocation baselines, respectively, significantly enhancing wireless access robustness and energy efficiency.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

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

Abstract

In critical situations such as natural disasters, network outages, battlefield communication, or large-scale public events, Unmanned Aerial Vehicles (UAVs) offer a promising approach to maximize wireless coverage for affected users in the shortest possible time. In this paper, we propose a novel framework where multiple UAVs are deployed with the objective to maximize the number of served user equipment (UEs) while ensuring a predefined data rate threshold. UEs are initially clustered using a K-means algorithm, and UAVs are optimally positioned based on the UEs' spatial distribution. To optimize power allocation and mitigate inter-cluster interference, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, considering both LOS and NLOS fading. Simulation results demonstrate that our method significantly enhances UEs coverage and outperforms Deep Q-Network (DQN) and equal power distribution methods, improving their UE coverage by up to 2.07 times and 8.84 times, respectively.

Problem

• Optimizes multi-UAV coverage for emergency scenarios
• Ensures power-efficient UE connectivity with data rate thresholds
• Reduces inter-cluster interference using MADDPG algorithm

Innovation

• K-means clusters UEs for optimal UAV positioning
• MADDPG optimizes power and reduces interference
• Combines LOS and NLOS fading for coverage