KAN-AE with Non-Linearity Score and Symbolic Regression for Energy-Efficient Channel Coding
Abstract
In this paper, we investigate Kolmogorov-Arnold network-based autoencoders (KAN-AEs) with symbolic regression (SR) for energy-efficient channel coding. By using SR, we convert KAN-AEs into symbolic expressions, which enables low-complexity implementation and improved energy efficiency at the radios. To further enhance the efficiency, we introduce a new non-linearity score term in the SR process to help select lower-complexity equations when possible. Through numerical simulations, we demonstrate that KAN-AEs achieve competitive BLER performance while improving energy efficiency when paired with SR. We score the energy efficiency of a KAN-AE implementation using the proposed non-linearity metric and compare it to a multi-layer perceptron-based autoencoder (MLP-AE). Our experiment shows that the KAN-AE paired with SR uses 1.38 times less energy than the MLP-AE, supporting that KAN-AEs are a promising choice for energy-efficient deep learning-based channel coding.
Cite
@article{arxiv.2601.01598,
title = {KAN-AE with Non-Linearity Score and Symbolic Regression for Energy-Efficient Channel Coding},
author = {Anthony Joseph Perre and Parker Huggins and Alphan Sahin},
journal= {arXiv preprint arXiv:2601.01598},
year = {2026}
}
Comments
IEEE Consumer Communications & Networking Conference 2026 (IEEE CCNC 2026), 9-12 January 2026, Las Vegas, NV, USA