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Full title—Lightweight and High-Performance Vehicular Channel Estimation with Liquid Neural Networks
Vehicular communication demands precise and efficient channel estimation for robust connectivity. While deep learning techniques, particularly long short-term memory (LSTM) networks, have recently improved channel estimation results by effectively capturing temporal dependencies, their high computational demands pose significant challenges, especially in resource-constrained environments such as Internet of Things devices.
This paper introduces a novel channel estimation approach using liquid neural networks to address these limitations. It specifically focuses on closed-form continuous-depth (CfC) networks optimized through a neural architecture search process to balance performance and complexity. The proposed CfC-based estimator enhances channel tracking accuracy and reduces computational overhead compared to traditional LSTM-based methods.
Full Article: IEEE Transactions on Vehicular Technology, Early Access
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