# Reflections in the Sky: Millimeter Wave Communication with UAV-Carried Intelligent Reflectors

arXiv: https://arxiv.org/abs/1908.03271 · HTML: https://mlsanity.com/paper/1908.03271/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks](https://mlsanity.com/paper/2102.01751/) (2021)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning](https://mlsanity.com/paper/2105.12823/) (2021)
- [LLMCount: Enhancing Stationary mmWave Detection with Multimodal-LLM](https://mlsanity.com/paper/2409.16209/) (2024)
- [Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces](https://mlsanity.com/paper/2104.00075/) (2021)

## Abstract (arXiv)

In this paper, a novel approach that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance the performance of millimeter wave (mmW) networks. In particular, the UAV-IR is used to intelligently reflect mmW beamforming signals from a base station towards a mobile outdoor user, while harvesting energy from mmW signals to power the IR. To maintain a line-of-sight (LOS) channel, a reinforcement learning (RL) approach, based on Q-learning and neural networks, is proposed to model the propagation environment, such that the location and reflection coefficient of the UAV-IR can be optimized to maximize the downlink transmission capacity. Simulation results show a significant advantage for using a UAV-IR over a static IR, in terms of the average data rate and the achievable downlink LOS probability. The results also show that the RL-based deployment of the UAV-IR further improves the network performance, relative to a scheme without learning.