数字通信中MLP与KAN神经接收机的公共损失参数效率分析
Common-Loss Parameter-Efficiency Analysis of MLP and KAN Neural Receivers for Digital Communications
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中文总结 AI 辅助
本研究以AWGN-BPSK为基准，比较MLP与KAN神经接收机，发现紧凑KAN以61.5%更少参数达到可比BER，利于实时部署。
中文摘要 AI 辅助
经典相干二进制相移键控（BPSK）在加性高斯白噪声（AWGN）信道下的接收在解析上已被充分理解，且最优硬判决检测器是已知的。因此，本研究使用神经接收机的目的并非取代经典的AWGN-BPSK检测器，而是特意选择AWGN-BPSK设置作为理论上可验证的基准，以分析神经接收机架构能以多紧凑的方式表示已知的决策行为。本文使用多信噪比（multi-SNR）奈奎斯特速率BPSK数据集，对多层感知器（MLP）和Kolmogorov-Arnold网络（KAN）接收机进行了比较。模型通过误码率（BER）、均方误差（MSE）和可训练参数数量进行评估。除了报告准确性之外，本工作还强调了一种公共损失参数效率视角：当两个接收机达到可比较的实际BER或损失区域时，参数较少的接收机对于实时部署更具吸引力。结果表明，MLP和KAN接收机均再现了预期的AWGN-BPSK检测趋势，而紧凑的KAN配置以显著更少的可训练参数达到了可比较的工作区域。特别是，具有3281个参数的KAN接收机实现了2.45 x 10^-4的测试BER，而具有8513个参数的MLP基线实现了2.50 x 10^-4的测试BER。这相当于在可比较的BER工作点下减少了约61.5%的可训练参数。这一减少对于实时神经接收机至关重要，因为它影响内存占用、参数访问、推理延迟、能耗以及嵌入式、软件定义无线电、FPGA、ASIC和边缘通信平台上的硬件可行性。
英文摘要
Classical coherent binary phase-shift keying (BPSK) reception over additive white Gaussian noise (AWGN) channels is analytically well understood, and the optimum hard-decision detector is known. Therefore, the aim of using neural receivers in this study is not to replace the classical AWGN-BPSK detector. Instead, the AWGN-BPSK setting is deliberately selected as a theoretically verifiable benchmark for analyzing how compactly neural receiver architectures can represent a known decision behavior. This paper compares multi-layer perceptron (MLP) and Kolmogorov--Arnold Network (KAN) receivers using a multi-SNR Nyquist-rate BPSK dataset. The models are evaluated using bit error rate (BER), mean squared error (MSE), and trainable parameter count. Beyond reporting accuracy alone, this work emphasizes a common-loss parameter-efficiency perspective: when two receivers reach a comparable practical BER or loss region, the receiver with fewer parameters is more attractive for real-time deployment. The results show that both MLP and KAN receivers reproduce the expected AWGN-BPSK detection trend, while a compact KAN configuration reaches a comparable operating region with substantially fewer trainable parameters. In particular, the KAN receiver with 3281 parameters achieves a test BER of 2.45 x 10^-4, while the MLP baseline with 8513 parameters achieves a test BER of 2.50 x 10^-4. This corresponds to approximately 61.5% fewer trainable parameters at a comparable BER operating point. This reduction is important for real-time neural receivers because it affects memory footprint, parameter access, inference latency, energy consumption, and hardware feasibility on embedded, software-defined radio, FPGA, ASIC, and edge communication platforms.
发表机构
- Ankara Yıldırım Beyazıt Üniversitesi（安卡拉耶尔德勒姆贝亚济特大学）
机构由 AI 辅助整理，请以论文原文为准。
