# Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels

arXiv: https://arxiv.org/abs/1910.02900 · HTML: https://mlsanity.com/paper/1910.02900/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 30 of 42)

- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [Federated mmWave Beam Selection Utilizing LIDAR Data](https://mlsanity.com/paper/2102.02802/) (2021)
- [Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments](https://mlsanity.com/paper/2203.01907/) (2022)
- [Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios](https://mlsanity.com/paper/2308.06868/) (2023)
- [Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2101.05206/) (2021)
- [LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training](https://mlsanity.com/paper/2104.14579/) (2021)
- [Denoising Noisy Neural Networks: A Bayesian Approach with Compensation](https://mlsanity.com/paper/2105.10699/) (2021)
- [M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models](https://mlsanity.com/paper/2506.14532/) (2025)
- [Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning](https://mlsanity.com/paper/2402.01259/) (2024)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Vision-Aided Beam Tracking: Explore the Proper Use of Camera Images with Deep Learning](https://mlsanity.com/paper/2109.14686/) (2021)
- [ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks](https://mlsanity.com/paper/2407.15023/) (2024)
- [Learning-Based UE Classification in Millimeter-Wave Cellular Systems With Mobility](https://mlsanity.com/paper/2109.05893/) (2021)
- [Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems](https://mlsanity.com/paper/2202.02247/) (2022)
- [Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems](https://mlsanity.com/paper/2203.04935/) (2022)
- [Intelligent Blockage Prediction and Proactive Handover for Seamless Connectivity in Vision-Aided 5G/6G UDNs](https://mlsanity.com/paper/2203.16419/) (2022)
- [Efficient Beam Search for Initial Access Using Collaborative Filtering](https://mlsanity.com/paper/2209.06669/) (2022)
- [Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback](https://mlsanity.com/paper/2305.08081/) (2023)
- [Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework](https://mlsanity.com/paper/2305.09739/) (2023)
- [Blockage Prediction in Directional mmWave Links Using Liquid Time Constant Network](https://mlsanity.com/paper/2306.04997/) (2023)
- [Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach](https://mlsanity.com/paper/2312.02184/) (2023)
- [CF-CGN: Channel Fingerprints Extrapolation for Multi-band Massive MIMO Transmission based on Cycle-Consistent Generative Networks](https://mlsanity.com/paper/2412.20885/) (2024)
- [To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks](https://mlsanity.com/paper/2507.17494/) (2025)
- [Transformer-Based Rate Prediction for Multi-Band Cellular Handsets](https://mlsanity.com/paper/2509.25722/) (2025)
- [AIRMap: AI-Generated Radio Maps for Wireless Digital Twins](https://mlsanity.com/paper/2511.05522/) (2025)
- [MMSense: Adapting Vision-based Foundation Model for Multi-task Multi-modal Wireless Sensing](https://mlsanity.com/paper/2511.12305/) (2025)
- [Wireless Dataset Similarity: Measuring Distances in Supervised and Unsupervised Machine Learning](https://mlsanity.com/paper/2601.01023/) (2026)
- [Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels](https://mlsanity.com/paper/2602.04703/) (2026)
- [Large Multimodal Model-Based Environment-Aware Mobility Management](https://mlsanity.com/paper/2607.09795/) (2026)
- [Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements](https://mlsanity.com/paper/2101.06886/) (2021)

## Abstract (arXiv)

Predicting the millimeter wave (mmWave) beams and blockages using sub-6GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. These gains attracted increasing interest in the last few years. Prior work, however, has focused on extracting spatial channel characteristics at the sub-6GHz band first and then use them to reduce the mmWave beam training overhead. This approach has a number of limitations: (i) It still requires a beam search at mmWave, (ii) its performance is sensitive to the error associated with extracting the sub-6GHz channel characteristics, and (iii) it does not normally account for the different dielectric properties at the different bands. In this paper, we first prove that under certain conditions, there exist mapping functions that can predict the optimal mmWave beam and correct blockage status directly from the sub-6GHz channel, which overcome the limitations in prior work. These mapping functions, however, are hard to characterize analytically which motivates exploiting deep neural network models to learn them. For that, we prove that a large enough neural network can use the sub-6GHz channel to directly predict the optimal mmWave beam and the correct blockage status with success probabilities that can be made arbitrarily close to one. Then, we develop an efficient deep learning model and empirically evaluate its beam/blockage prediction performance using the publicly available dataset DeepMIMO. The results show that the proposed solution can predict the mmWave blockages with more than 90% success probability. Further, these results confirm the capability of the proposed deep learning model in predicting the optimal mmWave beams and approaching the optimal data rates, that assume perfect channel knowledge, while requiring no beam training overhead...