# Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoff

arXiv: https://arxiv.org/abs/1807.02723 · HTML: https://mlsanity.com/paper/1807.02723/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2018.

## Built on by (most cited first, 11 of 11)

- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach](https://mlsanity.com/paper/2106.12556/) (2021)
- [Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments](https://mlsanity.com/paper/2203.01907/) (2022)
- [Machine Learning on Camera Images for Fast mmWave Beamforming](https://mlsanity.com/paper/2102.07337/) (2021)
- [RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption](https://mlsanity.com/paper/2312.02478/) (2023)
- [ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks](https://mlsanity.com/paper/2407.15023/) (2024)
- [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)
- [Handoff Design in User-Centric Cell-Free Massive MIMO Networks Using DRL](https://mlsanity.com/paper/2507.20966/) (2025)
- [Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements](https://mlsanity.com/paper/2101.06886/) (2021)
- [GCN-Based Throughput-Oriented Handover Management in Dense 5G Vehicular Networks](https://mlsanity.com/paper/2505.04894/) (2025)

## Abstract (arXiv)

The sensitivity of millimeter wave (mmWave) signals to blockages is a fundamental challenge for mobile mmWave communication systems. The sudden blockage of the line-of-sight (LOS) link between the base station and the mobile user normally leads to disconnecting the communication session, which highly impacts the system reliability. Further, reconnecting the user to another LOS base station incurs high beam training overhead and critical latency problems. In this paper, we leverage machine learning tools and propose a novel solution for these reliability and latency challenges in mmWave MIMO systems. In the developed solution, the base stations learn how to predict that a certain link will experience blockage in the next few time frames using their past observations of adopted beamforming vectors. This allows the serving base station to proactively hand-over the user to another base station with a highly probable LOS link. Simulation results show that the developed deep learning based strategy successfully predicts blockage/hand-off in close to 95% of the times. This reduces the probability of communication session disconnection, which ensures high reliability and low latency in mobile mmWave systems.