# Blockage Prediction in Directional mmWave Links Using Liquid Time Constant Network

arXiv: https://arxiv.org/abs/2306.04997 · HTML: https://mlsanity.com/paper/2306.04997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Liquid Time-constant Networks](https://mlsanity.com/paper/2006.04439/) (2020)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade](https://mlsanity.com/paper/2112.13187/) (2021)
- [Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/1807.02723/) (2018)
- [Prediction of mmWave/THz Link Blockages through Meta-Learning and Recurrent Neural Networks](https://mlsanity.com/paper/2106.07442/) (2021)
- [Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration](https://mlsanity.com/paper/2111.08242/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards Green AI-Native Networks: Evaluation of Neural Circuit Policy for Estimating Energy Consumption of Base Stations](https://mlsanity.com/paper/2504.02781/) (2025)

## Abstract (arXiv)

We propose to use a liquid time constant (LTC) network to predict the future blockage status of a millimeter wave (mmWave) link using only the received signal power as the input to the system. The LTC network is based on an ordinary differential equation (ODE) system inspired by biology and specialized for near-future prediction for time sequence observation as the input. Using an experimental dataset at 60 GHz, we show that our proposed use of LTC can reliably predict the occurrence of blockage and the length of the blockage without the need for scenario-specific data. The results show that the proposed LTC can predict with upwards of 97.85% accuracy without prior knowledge of the outdoor scenario or retraining/tuning. These results highlight the promising gains of using LTC networks to predict time series-dependent signals, which can lead to more reliable and low-latency communication.