# Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

arXiv: https://arxiv.org/abs/1803.09698 · HTML: https://mlsanity.com/paper/1803.09698/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs](https://mlsanity.com/paper/2407.06227/) (2024)
- [Machine Learning on Camera Images for Fast mmWave Beamforming](https://mlsanity.com/paper/2102.07337/) (2021)
- [Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications](https://mlsanity.com/paper/2301.00752/) (2023)
- [VOMTC: Vision Objects for Millimeter and Terahertz Communications](https://mlsanity.com/paper/2409.09330/) (2024)
- [Intelligent Blockage Prediction and Proactive Handover for Seamless Connectivity in Vision-Aided 5G/6G UDNs](https://mlsanity.com/paper/2203.16419/) (2022)
- [Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images](https://mlsanity.com/paper/2501.18618/) (2025)
- [Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems](https://mlsanity.com/paper/2506.22374/) (2025)
- [Large Multimodal Model-Based Environment-Aware Mobility Management](https://mlsanity.com/paper/2607.09795/) (2026)
- [Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework](https://mlsanity.com/paper/2604.02396/) (2026)

## Abstract (arXiv)

This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWave) networks. Since the received power on a mmWave link can attenuate aperiodically due to a human blockage, the long-term series of the future received power cannot be predicted by analyzing the received signals before the blockage occurs. We propose a novel mechanism that predicts a time series of the received power from the next moment to even several hundred milliseconds ahead. The key idea is to leverage the camera imagery and machine learning (ML). The time-sequential images can involve the spatial geometry and the mobility of obstacles representing the mmWave signal propagation. ML is used to build the prediction model from the dataset of sequential images labeled with the received power in several hundred milliseconds ahead of when each image is obtained. The simulation and experimental evaluations using IEEE 802.11ad devices and a depth camera show that the proposed mechanism employing convolutional LSTM predicted a time series of the received power in up to 500 ms ahead at an inference time of less than 3 ms with a root-mean-square error of 3.5 dB.