Frequency Matching in Spiking Neural Networks for mmWave Sensing

Problem
This preprint addresses the limitations of existing mmWave sensing methodologies that predominantly utilize artificial neural networks (ANNs). These methods often require extensive preprocessing and complex architectures to achieve robustness against the sparse, temporally irregular, and high-frequency noise characteristics of mmWave measurements. The authors identify a gap in the literature regarding the application of spiking neural networks (SNNs) in this domain, particularly in terms of their potential advantages in efficiency and performance on edge devices.

Method
The core technical contribution of this work is the introduction of a frequency-matching criterion for configuring the membrane decay factor in leaky integrate-and-fire (LIF) dynamics of SNNs. The authors analyze the interaction between the temporal filtering properties of LIF dynamics and the frequency structure of mmWave signals. By aligning the effective bandwidth of the LIF dynamics with the discriminative spectral content of the data, they derive a principled approach to enhance the performance of SNNs in mmWave sensing tasks. The experimental setup involves training SNNs on four widely used mmWave datasets, with a focus on optimizing the membrane decay factor based on the identified frequency-matching hypothesis.

Results
The proposed SNNs demonstrate a significant improvement in performance compared to ANN baselines. Specifically, the authors report an average test accuracy increase of 6.22% across the evaluated datasets. Additionally, the SNNs achieve a theoretical energy consumption reduction of 3.64× compared to the ANN counterparts, indicating a substantial efficiency gain. These results were obtained under a unified evaluation protocol, ensuring comparability across different models and datasets.

Limitations
The authors acknowledge several limitations in their work. Firstly, the study is constrained to mmWave sensing applications, and the generalizability of the frequency-matching criterion to other domains remains untested. Secondly, while the energy consumption reduction is theoretically significant, practical implementations may vary based on hardware constraints and real-world conditions. The authors do not address potential challenges in scaling SNNs for larger datasets or more complex sensing tasks, nor do they explore the impact of varying noise levels beyond the scope of their experiments.

Why it matters
This research has important implications for the development of efficient edge computing solutions in mmWave sensing applications. By demonstrating the advantages of SNNs over traditional ANNs, particularly in terms of robustness and energy efficiency, the work opens avenues for further exploration of SNNs in other domains where low-power, real-time processing is critical. The frequency-matching approach could serve as a foundational principle for future research aimed at optimizing SNN architectures for various signal processing tasks, potentially leading to broader adoption of spiking neural networks in practical applications.

Authors: Di Yu, Zhenyu Liao, Changze Lv, Wentao Tong, Linshan Jiang, Sijie Ji, Xin Du, Hailiang Zhao et al.
Source: arXiv:2605.09983
URL: https://arxiv.org/abs/2605.09983v1