Channel-Robust RFF for Low-Latency 5G Device Identification in SIMO Scenarios

• URL: http://arxiv.org/abs/2511.08902v1
• Date: Thu, 13 Nov 2025 01:15:59 GMT
• Title: Channel-Robust RFF for Low-Latency 5G Device Identification in SIMO Scenarios
• Authors: Yingjie Sun, Guyue Li, Hongfu Chou, Aiqun Hu,
• Abstract summary: Radio frequency fingerprint (RFF) identifies devices at the physical layer, blocking impersonation attacks while significantly reducing latency.<n>This paper introduces a new RFF extraction technique that employs signals from multiple receiving antennas to address multipath issues without adding latency.<n>The proposed scheme attains a 96.13% identification accuracy for 30 user equipments (UEs) within a 20-path channel under a signal-to-noise ratio (SNR) of 20 dB.
• Score: 12.602422198307815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultra-low latency, the hallmark of fifth-generation mobile communications (5G), imposes exacting timing demands on identification as well. Current cryptographic solutions introduce additional computational overhead, which results in heightened identification delays. Radio frequency fingerprint (RFF) identifies devices at the physical layer, blocking impersonation attacks while significantly reducing latency. Unfortunately, multipath channels compromise RFF accuracy, and existing channel-resilient methods demand feedback or processing across multiple time points, incurring extra signaling latency. To address this problem, the paper introduces a new RFF extraction technique that employs signals from multiple receiving antennas to address multipath issues without adding latency. Unlike single-domain methods, the Log-Linear Delta Ratio (LLDR) of co-temporal channel frequency responses (CFRs) from multiple antennas is employed to preserve discriminative RFF features, eliminating multi-time sampling and reducing acquisition time. To overcome the challenge of the reliance on minimal channel variation, the frequency band is segmented into sub-bands, and the LLDR is computed within each sub-band individually. Simulation results indicate that the proposed scheme attains a 96.13% identification accuracy for 30 user equipments (UEs) within a 20-path channel under a signal-to-noise ratio (SNR) of 20 dB. Furthermore, we evaluate the theoretical latency using the Roofline model, resulting in the air interface latency of 0.491 ms, which satisfies ultra-reliable and low-latency communications (URLLC) latency requirements.

Related papers

• Inter-Cell Interference Rejection Based on Ultrawideband Walsh-Domain Wireless Autoencoding [8.379012191212249]
  This paper proposes a novel technique for rejecting partial-in-band inter-cell interference (ICI) in ultrawideband communication systems.<n>We present the design of an end-to-end wireless autoencoder architecture that jointly optimize the transmitter and receiver encoding/decoding in the Walsh domain.
  arXiv  Detail & Related papers  (2026-01-16T19:00:52Z)
• A Zero Added Loss Multiplexing (ZALM) Source Simulation [34.91883081037593]
  We present a modular ZALM simulator that demonstrates how design choices affect output rate and fidelity.<n>Built in NetSquid with I/R controllers, it exposes 20+ parameters, supports I/R and REALISTIC modes, and provides reusable components.<n>Using the default configuration settings, average fidelity emains constant at 0.8 but the ebit rate decreases from 0.0175 at the source to 0.0 at 50 km; narrowing the SPDC degeneracy bandwidth increases the ebit rate significantly without affecting fidelity.
  arXiv  Detail & Related papers  (2025-10-29T22:41:40Z)
• Transmitter Identification via Volterra Series Based Radio Frequency Fingerprint [3.7848713545513317]
  Radio Frequency Fingerprinting (RFF) offers a promising solution as it requires no cryptography and resists spoofing.<n>Existing RFF approaches often lack a unified theory and effective feature extraction.<n>In this work, we model the transmitter as a black box and analyze its impact on transmitted signals.<n> 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.
  arXiv  Detail & Related papers  (2025-10-22T10:11:41Z)
• Dim and Small Target Detection for Drone Broadcast Frames Based on Time-Frequency Analysis [13.693769465573297]
  We propose a dim and small target detection algorithm for drone broadcast frames based on the time-frequency analysis of communication protocol.<n>The proposed algorithm improves the evaluation metrics by 2.27% compared to existing algorithms.
  arXiv  Detail & Related papers  (2025-05-14T02:53:42Z)
• Task-Oriented Feature Compression for Multimodal Understanding via Device-Edge Co-Inference [54.53508601749513]
  We propose a task-oriented feature compression (TOFC) method for multimodal understanding in a device-edge co-inference framework.<n>To enhance compression efficiency, multiple entropy models are adaptively selected based on the characteristics of the visual features.<n>Results show that TOFC achieves up to 52% reduction in data transmission overhead and 63% reduction in system latency.
  arXiv  Detail & Related papers  (2025-03-17T08:37:22Z)
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks.<n>Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance.<n>We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications [53.398544571833135]
  This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924. irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Our signal achieves faster data transmission within the same spectral bandwidth through 5G standard signal configuration.
  arXiv  Detail & Related papers  (2024-06-07T09:20:30Z)
• FrFT based estimation of linear and nonlinear impairments using Vision Transformer [10.615067455093516]
  It is essential to implement joint estimation of the following four critical impairments: nonlinear signal-to-noise ratio (SNRNL), optical signal-to-noise ratio (OSNR), chromatic dispersion (CD) and differential group delay (DGD) Our proposed method achieves accurate estimation of linear and nonlinear impairments over a broad range, representing a significant advancement in the field of optical performance monitoring (OPM)
  arXiv  Detail & Related papers  (2023-08-25T09:59:35Z)
• Deep Learning-Based Synchronization for Uplink NB-IoT [72.86843435313048]
  We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT) The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications.
  arXiv  Detail & Related papers  (2022-05-22T12:16:43Z)
• Real-Time GPU-Accelerated Machine Learning Based Multiuser Detection for 5G and Beyond [70.81551587109833]
  nonlinear beamforming filters can significantly outperform linear approaches in stationary scenarios with massive connectivity. One of the main challenges comes from the real-time implementation of these algorithms. This paper explores the acceleration of APSM-based algorithms through massive parallelization.
  arXiv  Detail & Related papers  (2022-01-13T15:20:45Z)
• Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning [56.94644428312295]
  Wireless connectivity is instrumental in enabling federated learning (FL) Channel randomnessperturbs each worker inversions model update while multiple workers updates incur significant interference on bandwidth. In A-FADMM, all workers upload their model updates to the parameter server using a single channel via analog transmissions. This not only saves communication bandwidth, but also hides each worker's exact model update trajectory from any eavesdropper.
  arXiv  Detail & Related papers  (2020-07-03T16:31:15Z)

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.