Multi-Link Prediction for mmWave Wireless Communication Systems Using Liquid Time-Constant Networks, Long Short- Term Memory, and Interpretation Using Symbolic Regression

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@Article{pendyala:2024:Electronics,

• author = "Vishnu S. Pendyala and Milind Patil",
• title = "Multi-Link Prediction for {mmWave} Wireless Communication Systems Using Liquid Time-Constant Networks, Long Short- Term Memory, and Interpretation Using Symbolic Regression",
• journal = "Electronics",
• year = "2024",
• volume = "13",
• number = "14",
• pages = "Article No. 2736",
• keywords = "genetic algorithms, genetic programming, LSTM, ANN",
• ISSN = "2079-9292",
• URL = "https://www.mdpi.com/2079-9292/13/14/2736",
• 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 using 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.",
• notes = "also known as \cite{electronics13142736}",

}

Genetic Programming entries for Vishnu S Pendyala Milind Patil

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