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

TL;DR

This paper introduces a Vision Transformer-based method for predicting signal blockages in mmWave networks, utilizing visual data and hierarchical fog-cloud computing to improve accuracy and reduce bandwidth consumption.

Contribution

It presents a novel visual-aided blockage prediction approach using ViT and a generative AI compression technique within a fog-cloud architecture, enhancing efficiency and accuracy.

Findings

Achieves 92.78% blockage prediction accuracy

Reduces bandwidth usage by 70.31%

Demonstrates effectiveness on real-world dataset

Abstract

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%.