# UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning

arXiv: https://arxiv.org/abs/2105.12823 · HTML: https://mlsanity.com/paper/2105.12823/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 78 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Convergence Analysis of Two-layer Neural Networks with ReLU Activation](https://mlsanity.com/paper/1705.09886/) (2017)
- [Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset](https://mlsanity.com/paper/2012.14036/) (2020)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1811.05053/) (2018)
- [Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles](https://mlsanity.com/paper/1905.00492/) (2019)
- [Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach](https://mlsanity.com/paper/1905.04152/) (2019)
- [Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs](https://mlsanity.com/paper/1509.01187/) (2015)
- [Reflections in the Sky: Millimeter Wave Communication with UAV-Carried Intelligent Reflectors](https://mlsanity.com/paper/1908.03271/) (2019)
- [Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning](https://mlsanity.com/paper/1911.05281/) (2019)
- [An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations](https://mlsanity.com/paper/1911.11343/) (2019)
- [A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks](https://mlsanity.com/paper/1904.07380/) (2019)
- [Real-time Framework for Trust Monitoring in aNetwork of Unmanned Aerial Vehicles](https://mlsanity.com/paper/2007.08590/) (2020)
- [Energy-Efficient Buffer-Aided Relaying Systems with Opportunistic Spectrum Access](https://mlsanity.com/paper/2002.11954/) (2020)

## Built on by (most cited first, 3 of 3)

- [A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management](https://mlsanity.com/paper/2401.02456/) (2024)
- [Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2306.10071/) (2023)
- [WONDER: A Radio World Model-based Negotiation Framework for Multi-Agent UAV Coverage Optimization](https://mlsanity.com/paper/2608.16955/) (2026)

## Abstract (arXiv)

The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users' information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.