# Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition

arXiv: https://arxiv.org/abs/2307.06834 · HTML: https://mlsanity.com/paper/2307.06834/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Global Filter Networks for Image Classification](https://mlsanity.com/paper/2107.00645/) (2021)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Coverage and Rate Analysis for Millimeter Wave Cellular Networks](https://mlsanity.com/paper/1402.6430/) (2014)
- [Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction](https://mlsanity.com/paper/2301.05837/) (2023)
- [26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone](https://mlsanity.com/paper/1905.00571/) (2019)
- [Prediction of mmWave/THz Link Blockages through Meta-Learning and Recurrent Neural Networks](https://mlsanity.com/paper/2106.07442/) (2021)
- [Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems](https://mlsanity.com/paper/2111.14805/) (2021)
- [Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures](https://mlsanity.com/paper/2211.09535/) (2022)
- [Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks](https://mlsanity.com/paper/2006.09902/) (2020)
- [Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications](https://mlsanity.com/paper/2301.00752/) (2023)
- [Machine Learning Empowered Beam Management for Intelligent Reflecting Surface Assisted MmWave Networks](https://mlsanity.com/paper/2003.01306/) (2020)
- [Performance of Predictive Indoor mmWave Networks with Dynamic Blockers](https://mlsanity.com/paper/2104.04623/) (2021)

## Built on by (most cited first, 2 of 2)

- [Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning](https://mlsanity.com/paper/2504.06173/) (2025)
- [RIS-Assisted Proactive Handover for Reliable mmWave Wireless Networks](https://mlsanity.com/paper/2606.26885/) (2026)

## Abstract (arXiv)

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track objects movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.