# Leveraging Sensing at the Infrastructure for mmWave Communication

arXiv: https://arxiv.org/abs/1911.09796 · HTML: https://mlsanity.com/paper/1911.09796/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

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

- [Deep learning for location based beamforming with NLOS channels](https://mlsanity.com/paper/2201.01386/) (2021)
- [Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System](https://mlsanity.com/paper/2402.09441/) (2024)
- [Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System](https://mlsanity.com/paper/2402.09440/) (2024)

## Abstract (arXiv)

Vehicle-to-everything (V2X) communication in the mmWave band is one way to achieve high data-rates for applications like infotainment, cooperative perception, and augmented reality assisted driving etc. MmWave communication relies on large antennas arrays, and configuring these arrays poses high training overhead. In this article, we motivate the use of infrastructure mounted sensors (which will be part of future smart cities) for mmWave communication. We provide numerical and measurement results to demonstrate that information from these infrastructure sensors reduces the mmWave array configuration overhead. Finally, we outline future research directions to help materialize the use of infrastructure sensors for mmWave communication.