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.

翻译：作为第五代移动通信（5G）标志性特征的超低延迟，对设备识别过程同样提出了严格的时序要求。当前基于密码学的解决方案会引入额外的计算开销，从而导致识别延迟增加。射频指纹（RFF）技术在物理层实现设备识别，既能有效抵御仿冒攻击，又能显著降低延迟。然而，多径信道会损害RFF的识别精度，而现有的信道鲁棒性方法需要跨多个时间点的反馈或信号处理，这又会带来额外的信令延迟。为解决这一问题，本文提出一种新的RFF特征提取技术，该技术利用多根接收天线的信号来解决多径问题，且不增加额外延迟。与单域方法不同，本方法采用来自多根天线的共时信道频率响应（CFR）的对数线性差值比（LLDR）来保留具有区分度的RFF特征，从而避免了多时间点采样并缩短了采集时间。为克服对最小信道变化依赖性的挑战，将频带划分为多个子带，并在每个子带内独立计算LLDR。仿真结果表明，在信噪比（SNR）为20 dB的20径信道环境下，所提方案对30个用户设备（UE）的识别准确率达到96.13%。此外，我们利用Roofline模型评估了理论延迟，得出空口延迟为0.491毫秒，满足超可靠低延迟通信（URLLC）的延迟要求。