Blockage Prediction in Directional mmWave Links Using Liquid Time Constant Network
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 8 Jun 2023.
Builds on
6 of 8 references.
References · 6
- 2020Liquid Time-constant Networkscited by 160
- 2019Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channelscited by 41
- 2021TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decadecited by 13
- 2018Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoffcited by 11
- 2021Prediction of mmWave/THz Link Blockages through Meta-Learning and Recurrent Neural Networkscited by 6
- 2021Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstrationcited by 4
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