# Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points

arXiv: https://arxiv.org/abs/2111.02258 · HTML: https://mlsanity.com/paper/2111.02258/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design](https://mlsanity.com/paper/1904.05242/) (2019)
- [REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association](https://mlsanity.com/paper/2010.01126/) (2020)
- [Coordinated Coverage and Fault Tolerance using Fixed-Wing Unmanned Aerial Vehicles](https://mlsanity.com/paper/2005.08153/) (2020)

## Built on by (most cited first, 4 of 4)

- [Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning](https://mlsanity.com/paper/2204.01597/) (2022)
- [Deep Reinforcement Learning for Combined Coverage and Resource Allocation in UAV-aided RAN-slicing](https://mlsanity.com/paper/2211.09713/) (2022)
- [Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks](https://mlsanity.com/paper/2306.08785/) (2023)
- [A hybrid solution for 2-UAV RAN slicing](https://mlsanity.com/paper/2409.11432/) (2024)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) promise to become an intrinsic part of next generation communications, as they can be deployed to provide wireless connectivity to ground users to supplement existing terrestrial networks. The majority of the existing research into the use of UAV access points for cellular coverage considers rotary-wing UAV designs (i.e. quadcopters). However, we expect fixed-wing UAVs to be more appropriate for connectivity purposes in scenarios where long flight times are necessary (such as for rural coverage), as fixed-wing UAVs rely on a more energy-efficient form of flight when compared to the rotary-wing design. As fixed-wing UAVs are typically incapable of hovering in place, their deployment optimisation involves optimising their individual flight trajectories in a way that allows them to deliver high quality service to the ground users in an energy-efficient manner. In this paper, we propose a multi-agent deep reinforcement learning approach to optimise the energy efficiency of fixed-wing UAV cellular access points while still allowing them to deliver high-quality service to users on the ground. In our decentralized approach, each UAV is equipped with a Dueling Deep Q-Network (DDQN) agent which can adjust the 3D trajectory of the UAV over a series of timesteps. By coordinating with their neighbours, the UAVs adjust their individual flight trajectories in a manner that optimises the total system energy efficiency. We benchmark the performance of our approach against a series of heuristic trajectory planning strategies, and demonstrate that our method can improve the system energy efficiency by as much as 70%.