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Kolmogorov-Arnold Networks outperform MLPs in FTN signaling detection

By PulseAugur Editorial · [1 sources] ·

A new research paper compares the effectiveness of Multilayer Perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs) for detecting Faster-than-Nyquist (FTN) signaling. The study generated a large dataset of nearly four million labeled windows for FTN BPSK detection under additive white Gaussian noise. Results indicate that KANs significantly outperform MLPs, achieving a much lower bit error rate with substantially fewer parameters. AI

IMPACT Kolmogorov-Arnold Networks demonstrate superior efficiency and performance over traditional MLPs for signal detection tasks, potentially influencing future research in communications.

RANK_REASON The cluster contains a research paper detailing a comparative analysis of neural network architectures for a specific signal processing task. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Kolmogorov-Arnold Networks outperform MLPs in FTN signaling detection

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The cluster contains a research paper detailing a comparative analysis of neural network architectures for a specific signal processing task. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sude Ertan, Osman Tokluoglu, Enver Cavus ·

    A Comparative Analysis of MLP and Kolmogorov-Arnold Networks (KAN) for Faster-than-Nyquist (FTN) Signaling Detection

    arXiv:2608.02062v1 Announce Type: cross Abstract: Faster-than-Nyquist signaling improves spectral ef- ficiency by deliberately introducing inter-symbol interference. Classical sequence detectors such as BCJR can approach optimal performance, but their computational cost grows rap…

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