Prediction of mmWave/THz Link Blockages through Meta-Learning and Recurrent Neural Networks

Anders E. Kalor, Osvaldo Simeone, Petar Popovski

Research output: Contribution to journalArticlepeer-review

Abstract

Wireless applications that rely on links that offer high reliability depend critically on the capability of the system to predict link quality within a given time interval. This dependence is especially acute at the high carrier frequencies used by mmWave and THz systems, where the links are susceptible to blockages. Predicting blockages with high reliability requires a large number of data samples to train effective machine learning modules. With the aim of mitigating data requirements, we introduce a framework based on meta-learning, whereby data from distinct deployments are leveraged to optimize a shared initialization that decreases the data set size necessary for any new deployment. Predictors of two different events are studied: (1) at least one blockage occurs in a time window, and (2) the link is blocked for the entire time window. The results show that an RNN-based predictor trained using meta-learning is able to predict blockages after observing fewer samples than predictors trained using standard methods.

Original languageEnglish (US)
JournalIEEE Wireless Communications Letters
DOIs
StateAccepted/In press - 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Blockage prediction
  • Fading channels
  • Industrial Internet of Things
  • Predictive models
  • Signal to noise ratio
  • Task analysis
  • Training
  • Wireless communication
  • meta-learning.
  • mmWave communication

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