Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification

• URL: http://arxiv.org/abs/2412.08885v1
• Date: Thu, 12 Dec 2024 02:48:20 GMT
• Title: Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification
• Authors: Rui Pan, Hui Chen, Guanxiong Shen, Hongyang Chen,
• Abstract summary: This paper proposes a residual channel-based data augmentation strategy for Radio Frequency Fingerprint Identification (RFFI) We show that our method significantly enhances both feature extraction ability and generalization while requiring fewer samples and less time.
• Score: 17.98760668117099
• License:
• Abstract: In order to address the issue of limited data samples for the deployment of pre-trained models in unseen environments, this paper proposes a residual channel-based data augmentation strategy for Radio Frequency Fingerprint Identification (RFFI), coupled with a lightweight SimSiam contrastive learning framework. By applying least square (LS) and minimum mean square error (MMSE) channel estimations followed by equalization, signals with different residual channel effects are generated. These residual channels enable the model to learn more effective representations. Then the pre-trained model is fine-tuned with 1% samples in a novel environment for RFFI. Experimental results demonstrate that our method significantly enhances both feature extraction ability and generalization while requiring fewer samples and less time, making it suitable for practical wireless security applications.

Related papers

• PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency [8.719356558714246]
  Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media. Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima. Various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process.
  arXiv  Detail & Related papers  (2025-02-17T15:30:17Z)
• Robust Representation Consistency Model via Contrastive Denoising [83.47584074390842]
  randomized smoothing provides theoretical guarantees for certifying robustness against adversarial perturbations. diffusion models have been successfully employed for randomized smoothing to purify noise-perturbed samples. We reformulate the generative modeling task along the diffusion trajectories in pixel space as a discriminative task in the latent space.
  arXiv  Detail & Related papers  (2025-01-22T18:52:06Z)
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think [72.48325960659822]
  One main bottleneck in training large-scale diffusion models for generation lies in effectively learning these representations. We study this by introducing a straightforward regularization called REPresentation Alignment (REPA), which aligns the projections of noisy input hidden states in denoising networks with clean image representations obtained from external, pretrained visual encoders. The results are striking: our simple strategy yields significant improvements in both training efficiency and generation quality when applied to popular diffusion and flow-based transformers, such as DiTs and SiTs.
  arXiv  Detail & Related papers  (2024-10-09T14:34:53Z)
• Generating High Dimensional User-Specific Wireless Channels using Diffusion Models [28.270917362301972]
  This paper introduces a novel method for generating synthetic wireless channel data using diffusion-based models. We generate synthetic high fidelity channel samples using user positions as conditional inputs, creating larger augmented datasets to overcome measurement scarcity.
  arXiv  Detail & Related papers  (2024-09-05T22:08:28Z)
• A Universal Deep Neural Network for Signal Detection in Wireless Communication Systems [35.07773969966621]
  Deep learning (DL) has been emerging as a promising approach for channel estimation and signal detection in wireless communications. To cope with the dynamic nature of the wireless channel, DL methods must be re-trained on newly non-aged collected data. This paper proposes a novel universal deep neural network (Uni-DNN) that can achieve high detection performance in various wireless environments without retraining the model.
  arXiv  Detail & Related papers  (2024-04-03T11:21:10Z)
• Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling [11.09458914721516]
  The complexity of channel modelling and the cost of collecting high-quality wireless channel data have become major challenges. We propose a diffusion model based channel sampling approach for rapidly realizations from limited data. We show that, compared to existing GAN based approaches which suffer from mode collapse and unstable training, our diffusion based approach trains synthesizingly and generates diverse and high-fidelity samples.
  arXiv  Detail & Related papers  (2023-08-10T13:49:26Z)
• Score-based Source Separation with Applications to Digital Communication Signals [72.6570125649502]
  We propose a new method for separating superimposed sources using diffusion-based generative models. Motivated by applications in radio-frequency (RF) systems, we are interested in sources with underlying discrete nature. Our method can be viewed as a multi-source extension to the recently proposed score distillation sampling scheme.
  arXiv  Detail & Related papers  (2023-06-26T04:12:40Z)
• Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics [77.13542705329328]
  We propose a framework of disentangled representation learning(DRL) that first learns to factor the input signals into a device-relevant component and a device-irrelevant component via adversarial learning. The implicit data augmentation in the proposed framework imposes a regularization on the RFF extractor to avoid the possible overfitting of device-irrelevant channel statistics. Experiments validate that the proposed approach, referred to as DR-RFF, outperforms conventional methods in terms of generalizability to unknown complicated propagation environments.
  arXiv  Detail & Related papers  (2022-08-04T15:46:48Z)
• Jo-SRC: A Contrastive Approach for Combating Noisy Labels [58.867237220886885]
  We propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution.
  arXiv  Detail & Related papers  (2021-03-24T07:26:07Z)
• Salvage Reusable Samples from Noisy Data for Robust Learning [70.48919625304]
  We propose a reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
  arXiv  Detail & Related papers  (2020-08-06T02:07:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.