Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs

TL;DR

This paper presents a multi-agent reinforcement learning approach for optimizing UAV trajectories to offload cellular data traffic, improving user association and network performance in IoT applications.

Contribution

It introduces a novel multi-agent reinforcement learning framework for joint UAV trajectory and user association optimization under QoS constraints.

Findings

UAV trajectories outperform benchmark methods like Q learning and particle swarm optimization.

The proposed method achieves higher average user association.

Simulation results validate the effectiveness of the approach.

Abstract

Effective solutions for intelligent data collection in terrestrial cellular networks are crucial, especially in the context of Internet of Things applications. The limited spectrum and coverage area of terrestrial base stations pose challenges in meeting the escalating data rate demands of network users. Unmanned aerial vehicles, known for their high agility, mobility, and flexibility, present an alternative means to offload data traffic from terrestrial BSs, serving as additional access points. This paper introduces a novel approach to efficiently maximize the utilization of multiple UAVs for data traffic offloading from terrestrial BSs. Specifically, the focus is on maximizing user association with UAVs by jointly optimizing UAV trajectories and users association indicators under quality of service constraints. Since, the formulated UAVs control problem is nonconvex and combinatorial, this study leverages the multi agent reinforcement learning framework. In this framework, each UAV acts as an independent agent, aiming to maintain inter UAV cooperative behavior. The proposed approach utilizes the finite state Markov decision process to account for UAVs velocity constraints and the relationship between their trajectories and state space. A low complexity distributed state action reward state action algorithm is presented to determine UAVs optimal sequential decision making policies over training episodes. The extensive simulation results validate the proposed analysis and offer valuable insights into the optimal UAV trajectories. The derived trajectories demonstrate superior average UAV association performance compared to benchmark techniques such as Q learning and particle swarm optimization.