PreprintArticleVersion 1This version is not peer-reviewed
Multi-Link Prediction for mmWave Wireless Communication Systems using Liquid Time-Constant Networks, Long Short-term Memory, and Interpretation using Symbolic Regression
Version 1
: Received: 30 June 2024 / Approved: 1 July 2024 / Online: 2 July 2024 (02:35:32 CEST)
How to cite:
Pendyala, V.; Patil, M. Multi-Link Prediction for mmWave Wireless Communication Systems using Liquid Time-Constant Networks, Long Short-term Memory, and Interpretation using Symbolic Regression. Preprints2024, 2024070128. https://doi.org/10.20944/preprints202407.0128.v1
Pendyala, V.; Patil, M. Multi-Link Prediction for mmWave Wireless Communication Systems using Liquid Time-Constant Networks, Long Short-term Memory, and Interpretation using Symbolic Regression. Preprints 2024, 2024070128. https://doi.org/10.20944/preprints202407.0128.v1
Pendyala, V.; Patil, M. Multi-Link Prediction for mmWave Wireless Communication Systems using Liquid Time-Constant Networks, Long Short-term Memory, and Interpretation using Symbolic Regression. Preprints2024, 2024070128. https://doi.org/10.20944/preprints202407.0128.v1
APA Style
Pendyala, V., & Patil, M. (2024). Multi-Link Prediction for mmWave Wireless Communication Systems using Liquid Time-Constant Networks, Long Short-term Memory, and Interpretation using Symbolic Regression. Preprints. https://doi.org/10.20944/preprints202407.0128.v1
Chicago/Turabian Style
Pendyala, V. and Milind Patil. 2024 "Multi-Link Prediction for mmWave Wireless Communication Systems using Liquid Time-Constant Networks, Long Short-term Memory, and Interpretation using Symbolic Regression" Preprints. https://doi.org/10.20944/preprints202407.0128.v1
Abstract
A significant challenge encountered in mmWave and Sub-terahertz systems used in 5G and 1 the upcoming 6G networks is the rapid fluctuation in signal quality across various beam directions. Extremely high-frequency waves are highly vulnerable to obstruction, making even slight adjustments in device orientation or the presence of blockers capable of causing substantial fluctuations in link quality along a designated path. This issue poses a major obstacle because numerous applications with low-latency requirements necessitate precise forecasting of network quality from many directions and cells. The method that is proposed in this research demonstrates an avant-garde approach for assessing the quality of multi-directional connections in mmWave systems by utilizing the Liquid Time Constant network (LTC) instead of the conventionally used Long Short-Term Memory (LSTM) technique. The method’s validity was tested through an optimistic simulation involving the monitoring of multi-cell connections at 28 GHz in a scenario where humans and various obstructions were moving arbitrarily. The results with LTC are significantly better than those obtained by conventional approaches such as LSTM. The latter resulted in a test Root Mean Squared Error (RMSE) of 3.44 dB,while the former, 0.25 dB, demonstrating a 13-fold improvement. For better interpretability and to illustrate the complexity of prediction, an approximate mathematical expression is also fitted to thesimulated signal data using Symbolic Regression.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
