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

2025-11-12

AI Summary

In 5G single-input multiple-output (SIMO) scenarios, radio frequency fingerprinting (RFF)-based device identification suffers from multipath fading, while existing robustness-enhancing methods incur excessive signaling latency, violating ultra-reliable low-latency communication (URLLC) requirements. Method: This paper proposes a low-latency robust RFF extraction method leveraging multi-antenna synchronized channel frequency responses (CFRs). It introduces a novel logarithmic linear differential ratio (LLDR) feature, enhanced by subband partitioning to improve sensitivity to subtle channel variations—without requiring multi-slot sampling or feedback mechanisms. Contribution/Results: Theoretical analysis based on the Roofline model confirms computational efficiency. Under challenging conditions (20-path fading, 20 dB SNR), the method achieves 96.13% identification accuracy across 30 devices, with an over-the-air latency of only 0.491 ms—significantly outperforming conventional cryptographic authentication and satisfying stringent URLLC latency constraints.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Addresses multipath channel degradation in RF fingerprinting for 5G identification
• Eliminates latency from multi-time sampling and cryptographic overhead in authentication
• Achieves high-accuracy device identification under strict URLLC timing constraints

Innovation

• Multi-antenna signal extraction for multipath mitigation
• Log-Linear Delta Ratio preserves RFF features without delay
• Sub-band frequency segmentation enhances channel robustness