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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1 Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA 11 Department of Applied Data Science, San Jose State University, San Jose  
relative to background noise. By predicting the quality, the device can seamlessly switch to the predicted high-quality link without losing the connection. However, owing to their short wavelength, EHF and THF waves have a strict Line of Sight requirement. This means that they cannot travel through walls or trees and are highly sensitive to obstacles. The channel changes very quickly as the user moves. Wireless systems using them are impacted by fluctuating channel quality when the beam propagates in various directions simultaneously. Millimeter-wave communication is also not energy efficient. Slight deviations in the path of the transceivers or the presence of interfering elements can cause significant alterations in link quality unpredictably. Low-latency applications necessitate the ability to accurately forecast the link quality across different cells and directions. Previous attempts to address the challenge of predicting multiple links using AI/ML techniques involved certain Recurrent Neural Network (RNN) models like Long short-term memory (LSTM) for the link prediction [5]. Link prediction is the important task of estimating the quality of the signal between nodes in a network. It plays an important role in EHF and THF systems because these signals are inherently flaky and unpredictable, but the low-latency applications require accurate prediction. Link prediction helps in optimizing network performance, making routing decisions, and resource allocation. Traditionally, the parameters for Artificial Neural Networks (ANNs), including RNNs like LSTM are fixed after training. ANNs use these fixed parameters to make decisions, a process called inferencing. Liquid Neural Networks (LNNs) are a better type of RNN that can learn even at the time of decision-making and are therefore better suited to real-world applications [6]. LNNs are a progression of Neural Ordinary Differential Equations (ODEs). Neural ODEs [7] use an approximation to evolve the hidden state by using a fixed time interval or a \"time constant\". Liquid Time-Constant Networks or LTCs are a special type of LNN that use a time constant that is not fixed, giving it the flexibility to dynamically adapt to changes in the data [8], which for this work is the wireless EHF and THF signal. We hypothesize that LTCs are better suited to the problem of multi-link prediction for mmWave and sub-terahertz because of the highly unpredictable nature of the signal, and the experiments detailed later confirm this. LTCs adapt to the rapid fluctuations in the data even during inferencing [9]. Their behavior is more explainable, they can learn from smaller amounts of data, and are computationally effective, requiring a smaller number of neurons [8]; hence, they can run on edge devices like smartphones. The simulation in this work is based on the Urban Microcellular Infrastructure (UMI) Channel model [10], an indoor scenario that typically uses 28 GHz as the operating carrier frequency. Therefore, the EHF and THF waves are generated for the experimental setup using a real-time simulation involving multiple links with trajectories of moving individuals and obstructing vehicles in a scenario set at 28 GHz. The simulation uses appropriate noise figure and path loss encompassing both Line-of-Sight (LOS) and Non-Line-of-Sight (NLoS) propagation paths following the 3 GPP Channel model [11]. The parameters set for the simulation are as specified in Table 1. Also, the current literature [5] uses 28 GHz for experiments with LSTM. Using the same 28 GHz frequency for this work helps ensure a fair evaluation of the results.