发表机构
Ankara Yıldırım Beyazıt University（安卡拉耶尔德勒姆贝亚兹特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文对比MLP与KAN在FTN BPSK检测中的性能，基于近400万样本的实验显示，KAN误码率较MLP低18.6倍且参数仅为其1/8，是更优的神经决策模型。
AI 中文摘要
超奈奎斯特（FTN）信号通过刻意引入码间干扰来提升频谱效率。BCJR等经典序列检测器可逼近最优性能，但其计算成本随信道记忆长度快速增长。本文研究加性高斯白噪声（AWGN）下的FTN二进制相移键控（BPSK）检测，直接对比多层感知器（MLP）与Kolmogorov-Arnold网络（KAN）的性能。针对时间打包因子0.8、信噪比7至10分贝的场景，生成包含近400万个带标签窗口的大规模蒙特卡洛数据集。经宽度搜索得到的最优MLP使用隐藏层宽度32，所选KAN使用隐藏层宽度4、样条网格大小5。在10分贝时，MLP的误码率为1.3×10⁻⁴，KAN的误码率为7×10⁻⁶；这意味着KAN误码率降低18.6倍，且仅使用MLP八分之一的隐藏宽度。结果表明，在FTN BPSK检测任务中，KAN相比MLP基线是更有效、参数效率更高的神经决策模型。
英文摘要
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.
CommentsPresented at the 34th IEEE Signal Processing and Communications Applications Conference (SIU 2026)
