KAN比MLP更高效地检测高速信号，误差更低且参数少得多。
A Comparative Analysis of MLP and Kolmogorov-Arnold Networks (KAN) for Faster-than-Nyquist (FTN) Signaling Detection
- 用KAN和MLP直接对比，做高速信号的神经检测。
- 在10dB下，KAN误码率是MLP的1/18.6，仅需1/8参数量。
- 适合通信系统设计者，尤其关注低复杂度高精度检测的场景。
高速奈奎斯特信号通过故意引入符号间干扰提升频谱效率。经典序列检测器如BCJR可接近最优性能，但计算开销随信道记忆长度急剧上升。本文在加性白高斯噪声（AWGN）环境下，通过大规模蒙特卡洛仿真数据集（近四百万个标签样本，时间压缩因子0.8，信噪比7~10dB），对多层感知机（MLP）与科尔莫戈罗夫-阿诺德网络（KAN）在高速二进制相移键控（FTN BPSK）检测中的表现进行直接比较。最佳MLP采用32维隐藏层宽度，而选定的KAN使用4维隐藏层宽度与5×5样条网格。在10dB时，MLP误码率为1.3×10⁻⁴，而KAN达到7×10⁻⁶，误码率降低18.6倍，且仅使用1/8的参数量。结果表明，KAN在高速信号检测中相较MLP具有更高的有效性与更低的参数复杂度。
原文摘要 · Abstract (English)
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 rapidly with channel memory. This paper investigates data-driven FTN BPSK detection under AWGN through a direct comparison between multilayer perceptrons and Kolmogorov Arnold Networks. A large-scale Monte Carlo dataset containing nearly four million labeled windows is generated for a time-packing factor of zero point eight and signal-to-noise ratio values from seven to ten decibels. The best MLP obtained from width sweeping uses hidden width thirty two, whereas the selected KAN uses hidden width four with spline grid size five. At ten decibels, the MLP produces a bit error rate of one point three times ten to the minus four, while the KAN reaches seven times ten to the minus six. This corresponds to an eighteen point six times lower bit error rate while using only one eighth of the MLP hidden width. The results show that KAN provides a more effective and more parameter-efficient neural decision model than the MLP baseline for FTN BPSK detection.
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