关键词:
Authentication
chipless RFID tags
data fore-cast
data fore-cast
encoding scheme
high encoding capacity
high encoding capacity
robustness
robustness
machine learning
machine learning
machine learning
摘要:
This article presents a robust, high-capacity chipless encoding solution based on mapping resonance frequencies to Euclidean space. We introduce, for the first time, a methodology that exploits a usually undesired phenomenon, i.e., mutual coupling, as a method to improve the encoding capacity of chipless RFID tags. A deep analysis of the proposed method's capability to mitigate fabrication tolerance and measurement uncertainties is performed. To address the computational challenges of the decoding phase, we employ machine learning to predict resonant frequencies of noncalibrated tags. With a tolerance of 50 MHz, a forecast accuracy of 97.6% within a 25 MHz error margin, and 100% within a 40 MHz error margin is achieved. A reliable space encoding efficiency of 22.5 bits/cm(2 )and spectrum encoding efficiency of 2.1 bits/GHz are achieved. As a proof of concept, we designed tags using periodically arranged, middle-notched planar dipole resonators, chosen for their fabrication-tolerant characteristics, enhanced radar cross section (RCS) level and back-shielding properties.