A novel methodology is proposed to reliably predict the resonant characteristics of a multipatch backscatter-based radio frequency identification (RFID) chipless tag. An ultra-wideband impulsion radio (UWB-IR)-based reader interrogates the chipless tag with a UWB pulse, and analyzes the obtained backscatter in the time domain. The RFID system consists of a radar cross-section (RCS)-based chipless tag containing a square microstrip patch antenna array in which the chipless tag is interrogated with a UWB pulse by an UWB-IR-based reader. The main components of the backscattered signal, the structural mode, and the antenna mode were identified and their spectral quality was evaluated. The study revealed that the antenna-mode backscatter includes signal carrying information, while the structural mode backscatter does not include any tag information. The simulation findings were confirmed by experimental measurements obtained in an anechoic chamber environment using a 6-bit multipatch chipless RFID tag. Finally, the novel technique does not use calibration tags and can freely orient tags with respect to the reader.

In order to encrypt/encode data based on the magnitude level of the radar cross-section (RCS), we propose an approach with a precise estimation considering the resonant characteristics of a multipatch backscatter-based chipless radio frequency identification (RFID) dedicated for chipless tags depolarization. The working principle is based on the polarization mismatch between the tag and the reader antenna to control the magnitude of the backscatter, which allows a reliable detection in real environments. We introduce in this paper a new 4-bit chipless RFID tag with an enhanced RCS, based on a triangular patch antenna with multiple resonators. Additionally, we propose an ultra-wideband impulse radar (UWB-IR)-based reader that interrogates the chipless tag with a UWB pulse, and the received backscatter was studied in both time- and frequency-domains. The antenna was operating from 4.7 to 6.1 GHz, a band allocated for RFID systems. The obtained experimental measurement results in the environment of anechoic chamber were exceptionally relevant to validate the simulation results.

Chipless RFID has the potential to bring in a paradigm change in industrial applications, notably in the world of the Internet of Things (IoT). This wireless technology permits distant identification, sensing, and tracking, giving tremendous development prospects. Despite substantial advances over the previous century, designing high-performance chipless RFID for IoT applications remains a problem. This research presents a new chipless planar RFID tag for item tracking and identification in IoT systems. The suggested tag uses numerous T-shaped slow-wave structures as micro-reflecting resonators with different dimensions on a lossy substrate to encode information in the tag’s backscattered signal after illumination. The mutual coupling between the resonators has been minimized to improve tag performance and printing variety. A tag with a coding capacity of 24 bits and a compact size of 60 x 40 mm ² was simulated in CST Microwave Studio to validate the suggested approach. To create tags, several patterned configurations were applied on a Rogers RO4350B substrate, and their radar cross-section responses were studied. When compared to typical multi-resonator tags, the suggested tag design demonstrated excellent downsizing and efficient frequency spectrum usage. Furthermore, the Q-factor and coding strength were both high, showing that the suggested technology is capable of manufacturing high-performance chipless tags.

This study presents a machine learning (ML)-enhanced framework for the detection, decoding, and localization of chipless radio-frequency identification (RFID) tags using cost-efficient ultrawideband impulse radio (UWB-IR) readers. By combining frequency-domain backscatter analysis with round-trip time-of-flight (RTOF) measurements, we propose an advanced signal processing architecture that significantly improves localization accuracy. To further enhance system robustness in noisy or multipath environments, a supervised ML classifier is integrated into the identification pipeline. Key features extracted from antenna mode backscatter signals, such as resonant amplitudes, signal-to-noise ratio (SNR) metrics, and temporal characteristics are used to train a support vector machine (SVM), achieving over 96% identification accuracy at 10 dB SNR, and maintaining high performance even under challenging conditions. This design improves resonance stability, environmental durability, and omnidirectional readability, making it highly suitable for real-time Internet of Things (IoT) applications, such as asset tracking and smart infrastructure monitoring. The proposed system is validated through comprehensive electromagnetic simulations in CST Studio Suite and MATLAB, as well as experimental measurements in anechoic chamber setups. Results demonstrate consistent performance, with a detection range of 30 cm and angular resolution of 60°, even under varying environmental conditions.

In this study, we present a new circular monopole antenna with triple-band characteristics that can operate in both the ultra-high frequency (UHF) and ultra-wideband (UWB) bands and is integrated into a radio frequency identification (RFID) system reader module. In fact, the antenna was equipped with a patch and a CPW feed to meet the bandwidth specifications for RFID applications at UHF/UWB. To connect with RFID tags, this antenna is manufactured and inserted into the RFID reader board. Experiments show that the UHF-RFID efficiency of the monopole antenna is comparable to that of current commercial solutions.

This dataset comprises over 55,000 simulation samples of a microstrip patch antenna engineered for operation near the 2.4 GHz frequency band—a key spectrum for wireless communication and IoT applications. Each sample records S11 reflection coefficient values (in decibels), mapping the return loss across a broad range of geometric configurations. Data were generated over a one-month period using parameter sweeps and variation studies with CST Microwave Studio, ensuring comprehensive coverage of design possibilities. The resulting dataset serves as a substantial resource for developing and benchmarking machine learning models in antenna performance prediction, design automation, and optimization tasks including impedance matching, bandwidth enhancement, and geometric optimization in RF and microwave engineering. To promote generalizability and practical relevance for the research community, future dataset releases will incorporate experimental validation and expand target parameters to include antenna impedance, thereby improving the robustness and utility for advanced AI-driven modeling and optimization.