# Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication

arXiv: https://arxiv.org/abs/2501.11763 · HTML: https://mlsanity.com/paper/2501.11763/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2025.

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

- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Conditional Denoising Diffusion for ISAC Enhanced Channel Estimation in Cell-Free 6G](https://mlsanity.com/paper/2506.06942/) (2025)

## Abstract (arXiv)

In mmWave wireless networks, signal blockages present a significant challenge due to the susceptibility to environmental moving obstructions. Recently, the availability of visual data has been leveraged to enhance blockage prediction accuracy in mmWave networks. In this work, we propose a Vision Transformer (ViT)-based approach for visual-aided blockage prediction that intelligently switches between mmWave and Sub-6 GHz frequencies to maximize network throughput and maintain reliable connectivity. Given the computational demands of processing visual data, we implement our solution within a hierarchical fog-cloud computing architecture, where fog nodes collaborate with cloud servers to efficiently manage computational tasks. This structure incorporates a generative AI-based compression technique that significantly reduces the volume of visual data transmitted between fog nodes and cloud centers. Our proposed method is tested with the real-world DeepSense 6G dataset, and according to the simulation results, it achieves a blockage prediction accuracy of 92.78% while reducing bandwidth usage by 70.31%.