Performance of Predictive Indoor mmWave Networks with Dynamic Blockers

TL;DR

This paper introduces a machine learning-based beam recovery method for indoor mmWave networks in factories, significantly improving signal stability and data rates amid dynamic blockers like humans and robots.

Contribution

It proposes a novel ML-driven beam prediction and recovery procedure that reduces delays and enhances link reliability in dynamic indoor environments.

Findings

Achieves up to 82% data rate improvement over detection-based methods.

Provides higher signal stability during rapid blocker movements.

Uses synthetic data and DNN models for real-time blockage prediction.

Abstract

In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then re-establish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed system-level simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams' blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantee higher signal level stability and up to $82\%$ data rate improvement with respect detection-based methods when blockers move at speed of $2$ m/s.