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

arXiv: https://arxiv.org/abs/2106.07442 · HTML: https://mlsanity.com/paper/2106.07442/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2021.

## Built on by (most cited first, 6 of 6)

- [Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization](https://mlsanity.com/paper/2108.00785/) (2021)
- [Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition](https://mlsanity.com/paper/2307.06834/) (2023)
- [Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework](https://mlsanity.com/paper/2305.09739/) (2023)
- [Blockage Prediction in Directional mmWave Links Using Liquid Time Constant Network](https://mlsanity.com/paper/2306.04997/) (2023)
- [To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks](https://mlsanity.com/paper/2507.17494/) (2025)
- [Online Bayesian Meta-Learning for Cognitive Tracking Radar](https://mlsanity.com/paper/2207.06917/) (2022)

## Abstract (arXiv)

Wireless applications that use high-reliability low-latency links depend critically on the capability of the system to predict link quality. 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.