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 language | English |
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Article number | 9562750 |
Journal | I E E E Wireless Communications Letters |
Volume | 10 |
Issue number | 12 |
Pages (from-to) | 2815-2819 |
Number of pages | 5 |
ISSN | 2162-2337 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Blockage prediction
- Fading channels
- Industrial Internet of Things
- Predictive models
- Signal to noise ratio
- Task analysis
- Training
- Wireless communication
- meta-learning.
- mmWave communication