Deep Learning based Cross-Receiver Radio Frequency Fingerprint Identification Under Varying Channels

Radio frequency fingerprint identification (RFFI) exploits device-specific hardware impairments for transmitter recognition, but its performance is highly vulnerable to receiver variations and changing wireless channels in cross-receiver deployment. To address both challenges, this paper proposes a novel cross-receiver RFFI framework with channel robustness. In the enrollment stage, a channel-robust preprocessing method is developed to construct denoised spectral quotient (DSQ) sequences, and a DSQ-based convolutional neural network (DSQCNN) is trained using data collected from the source receiver. In the cross-receiver deployment stage, a calibration dataset is built from signals captured by both the source and target receivers, and a trainable calibration neural network (TCNN) is designed to learn the nonlinear mapping between them. The cascaded TCNN-DSQCNN framework then enables robust transmitter classification on the target receiver under varying channel conditions. To the best of our knowledge, this is the first work to jointly address channel and receiver portability through combined channel suppression and nonlinear receiver calibration. Simulations with twelve WiFi transmitters and three receivers show that the proposed method achieves reliable cross-receiver classification, reaching over 90\% accuracy at an SNR of 24 dB.

CSRD2025: A Large-Scale Synthetic Radio Dataset for Spectrum Sensing in Wireless Communications

The development of Large AI Models (LAMs) for wireless communications, particularly for complex tasks like spectrum sensing, is critically dependent on the availability of vast, diverse, and realistic datasets. Addressing this need, this paper introduces the ChangShuoRadioData (CSRD) framework, an open-source, modular simulation platform designed for generating large-scale synthetic radio frequency (RF) data. CSRD simulates the end-to-end transmission and reception process, incorporating an extensive range of modulation schemes (100 types, including analog, digital, OFDM, and OTFS), configurable channel models featuring both statistical fading and site-specific ray tracing using OpenStreetMap data, and detailed modeling of realistic RF front-end impairments for various antenna configurations (SISO/MISO/MIMO). Using this framework, we characterize CSRD2025, a substantial dataset benchmark comprising over 25,000,000 frames (approx. 200TB), which is approximately 10,000 times larger than the widely used RML2018 dataset. CSRD2025 offers unprecedented signal diversity and complexity, specifically engineered to bridge the Sim2Real gap. Furthermore, we provide processing pipelines to convert IQ data into spectrograms annotated in COCO format, facilitating object detection approaches for time-frequency signal analysis. The dataset specification includes standardized 8:1:1 training, validation, and test splits (via frame indices) to ensure reproducible research. The CSRD framework is released at https://github.com/Singingkettle/ChangShuoRadioData to accelerate the advancement of AI-driven spectrum sensing and management.

Channel-robust Automatic Modulation Classification Using Spectral Quotient Cumulants

Automatic modulation classification (AMC) is to identify the modulation format of the received signal corrupted by the channel effects and noise. Most existing works focus on the impact of noise while relatively little attention has been paid to the impact of channel effects. However, the instability posed by multipath fading channels leads to significant performance degradation. To mitigate the adverse effects of the multipath channel, we propose a channel-robust modulation classification framework named spectral quotient cumulant classification (SQCC) for orthogonal frequency division multiplexing (OFDM) systems. Specifically, we first transform the received signal to the spectral quotient (SQ) sequence by spectral circular shift division operations. Secondly, an outlier detector is proposed to filter the outliers in the SQ sequence. At last, we extract spectral quotient cumulants (SQCs) from the filtered SQ sequence as the inputs to train the artificial neural network (ANN) classifier and use the trained ANN to make the final decisions. Simulation results show that our proposed SQCC method exhibits classification robustness and superiority under various unknown Rician multipath fading channels compared with other existing methods. Specifically, the SQCC method achieves nearly 90% classification accuracy at the signal to noise ratio (SNR) of 4dB when testing under multiple channels but training under AWGN channel.

Exemplar Loss for Siamese Network in Visual Tracking

Visual tracking plays an important role in perception system, which is a crucial part of intelligent transportation. Recently, Siamese network is a hot topic for visual tracking to estimate moving targets' trajectory, due to its superior accuracy and simple framework. In general, Siamese tracking algorithms, supervised by logistic loss and triplet loss, increase the value of inner product between exemplar template and positive sample while reduce the value of inner product with background sample. However, the distractors from different exemplars are not considered by mentioned loss functions, which limit the feature models' discrimination. In this paper, a new exemplar loss integrated with logistic loss is proposed to enhance the feature model's discrimination by reducing inner products among exemplars. Without the bells and whistles, the proposed algorithm outperforms the methods supervised by logistic loss or triplet loss. Numerical results suggest that the newly developed algorithm achieves comparable performance in public benchmarks.