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

arXiv: https://arxiv.org/abs/2402.02957 · HTML: https://mlsanity.com/paper/2402.02957/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks](https://mlsanity.com/paper/1810.10408/) (2018)
- [What Will 5G Be?](https://mlsanity.com/paper/1405.2957/) (2014)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks with Sense-and-Send Protocol](https://mlsanity.com/paper/1809.02934/) (2018)
- [RISs and Sidelink Communications in Smart Cities: The Key to Seamless Localization and Sensing](https://mlsanity.com/paper/2301.03535/) (2023)
- [Motion control for autonomous heterogeneous multi-agent area search in uncertain conditions](https://mlsanity.com/paper/1911.09137/) (2019)
- [A QoS-Oriented Trajectory Optimization in Swarming Unmanned-Aerial-Vehicles Communications](https://mlsanity.com/paper/1904.07988/) (2019)
- [Utility-Aware Optimal Resource Allocation Protocol for UAV-Assisted Small Cells with Heterogeneous Coverage Demands](https://mlsanity.com/paper/1911.00821/) (2019)
- [Optimal Green Hybrid Attacks in Secure IoT](https://mlsanity.com/paper/1912.02097/) (2019)

## Built on by (most cited first, 2 of 2)

- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)

## Abstract (arXiv)

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.