arXiv subjects

Shiyou Xu

Publications and source records attributed to Shiyou Xu.

3 recordsLinked to original sources

arXiv2026

Study of Class-Incremental Radio Frequency Fingerprint Recognition Without Storing Exemplars

The rapid proliferation of wireless devices makes robust identity authentication essential. Radio Frequency Fingerprinting (RFF) exploits device-specific, hard-to-forge physical-layer impairments for identification, and is promising for IoT and unmanned systems. In practice, however, new devices continuously join deployed systems while per-class training data are limited. Conventional static training and naive replay of stored exemplars are impractical due to growing class cardinality, storage cost, and privacy concerns. We propose an exemplar-free class-incremental learning framework tailored to RFF recognition. Starting from a pretrained feature extractor, we freeze the backbone during incremental stages and train only a classifier together with lightweight Adapter modules that perform small task-specific feature adjustments. For each class we fit a diagonal Gaussian Mixture Model (GMM) to the backbone features and sample pseudo-features from these fitted distributions to rehearse past classes without storing raw signals. To improve robustness under few-shot conditions we introduce a time-domain random-masking augmentation and adopt a multi-teacher distillation scheme to compress stage-wise Adapters into a single inference Adapter, trading off accuracy and runtime efficiency. We evaluate the method on large, self-collected ADS-B datasets: the backbone is pretrained on 2,175 classes and incremental experiments are run on a disjoint set of 669 classes with multiple rounds and step sizes. Against several representative baselines, our approach consistently yields higher average accuracy and lower forgetting, while using substantially less storage and avoiding raw-data retention. The proposed pipeline is reproducible and provides a practical, low-storage solution for RFF deployment in resource- and privacy-constrained environments.

eess.SP↗

arXiv2025

Transmitter Identification via Volterra Series Based Radio Frequency Fingerprint

The growing number of wireless devices increases the need for secure network access. Radio Frequency Fingerprinting (RFF), a physical-layer authentication method, offers a promising solution as it requires no cryptography and resists spoofing. However, existing RFF approaches often lack a unified theory and effective feature extraction. Many methods use handcrafted signal features or direct neural network classification, leading to limited generalization and interpretability. In this work, we model the transmitter as a black box and analyze its impact on transmitted signals. By treating the deviation from an ideal signal as hardware-induced distortion, we represent the received signal using a Volterra series, using its kernels to capture linear and nonlinear hardware traits. To manage the high dimensionality of these kernels, we approximate them via wavelet decomposition and estimate coefficients through least-squares fitting. The resulting wavelet coefficients provide compact yet informative hardware representations, which are classified using a complex-valued neural network. Experiments on a public LoRa dataset show state-of-the-art performance, with over 98% accuracy in static channels and above 90% under multipath and Doppler effects. The proposed approach improves both interpretability and generalization across varying channel conditions.

cs.CR↗

arXiv2025

STEM: Efficient Relative Capability Evaluation of LLMs through Structured Transition Samples

Evaluating large language models (LLMs) has become increasingly challenging as model capabilities advance rapidly. While recent models often achieve higher scores on standard benchmarks, these improvements do not consistently reflect enhanced real-world reasoning capabilities. Moreover, widespread overfitting to public benchmarks and the high computational cost of full evaluations have made it both expensive and less effective to distinguish meaningful differences between models. To address these challenges, we propose the \textbf{S}tructured \textbf{T}ransition \textbf{E}valuation \textbf{M}ethod (STEM), a lightweight and interpretable evaluation framework for efficiently estimating the relative capabilities of LLMs. STEM identifies \textit{significant transition samples} (STS) by analyzing consistent performance transitions among LLMs of the same architecture but varying parameter scales. These samples enable STEM to effectively estimate the capability position of an unknown model. Qwen3 model family is applied to construct the STS pool on six diverse and representative benchmarks. To assess generalizability. Experimental results indicate that STEM reliably captures performance trends, aligns with ground-truth rankings of model capability. These findings highlight STEM as a practical and scalable method for fine-grained, architecture-agnostic evaluation of LLMs.

cs.CL↗