Publication Date

7-1-2024

Document Type

Article

Publication Title

Electronics (Switzerland)

Volume

13

Issue

14

DOI

10.3390/electronics13142736

Abstract

A significant challenge encountered in mmWave and sub-terahertz systems used in 5G and 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 the precise forecasting of network quality from many directions and cells. The method 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 monitoring 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 the simulated signal data using Symbolic Regression.

Keywords

5G network, extremely high frequency, genetic programming, liquid neural networks, mmWave

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Applied Data Science

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