IEEE journal of radio frequency identification最新文献

{"title":"Deep Learning-Based Secure Tag Selection in BackCom Network With RIS-Induced Interference","authors":"Yasin Khan;Shalini Tripathi;Ankit Dubey;Sudhir Kumar;Sunish Kumar Orappanpara Soman","doi":"10.1109/JRFID.2025.3611299","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3611299","url":null,"abstract":"This article investigates the secrecy performance of a non-linear energy-harvesting backscatter communication (BackCom) network in the presence of direct link and reconfigurable intelligent surface (RIS) interference. The network comprises a source, multiple passive tags, an RIS, and a legitimate reader, with an eavesdropper attempting to intercept the communication. We analyze a tag selection scheme based on long-short-term memory (LSTM) to address the challenge of selecting tags under the influence of direct link and the RIS interference. The nonideal behavior of the RIS is exploited to enhance secrecy performance by modeling RIS phase errors using Von Mises and uniform distributions. Because of interference from the direct link and the RIS being common to all tags, the secrecy rates of different tags are correlated. The LSTM-based scheme effectively captures this correlation and perfectly matches the conventional selection scheme on low and high tag counts. The secrecy outage probability (SOP) achieved using the LSTM outperforms other machine learning techniques, such as <inline-formula> <tex-math>$k$ </tex-math></inline-formula>-nearest neighbors (<inline-formula> <tex-math>$k$ </tex-math></inline-formula>-NN), decision trees (DT), and support vector machines (SVM). We also demonstrate the impact of RIS elements, phase error parameters, and the number of tags on the SOP in the considered RIS-aided BackCom network.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"797-806"},"PeriodicalIF":3.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid DCNN-Enabled Depolarizing Chipless RFID: Improving Tag Detection Across Varying Lossy Surfaces and Shapes","authors":"Nadeem Rather;Roy B. V. B. Simorangkir;Dinesh R. Gawade;John L. Buckley;Brendan O’Flynn;Salvatore Tedesco","doi":"10.1109/JRFID.2025.3608617","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3608617","url":null,"abstract":"This paper presents a comprehensive design and implementation approach for robust detection of depolarizing chipless RFID (CRFID) tags. Depolarizing tags are advantageous compared to co-polar CRFID tags due to their improved performance on RF-lossy materials. This work introduces the application of deep learning (DL) regression modelling to a specialised dataset of depolarised Radar Cross Section (RCS) measurements of a custom 3-bit CRFID tag, acquired through an extensive robot-based data acquisition method. A dataset of 12,600 depolarised Electromagnetic (EM) RCS signatures were collected using an automated data acquisition system to train and validate a 1-dimensional Convolutional Neural Network (1D CNN) architecture. A novel hybrid 1D CNN with Bi-LSTM and attention mechanism architecture was also implemented to visualize the model attention and improve detection performance. We present, for the first time reported in literature, a comprehensive design and AI implementation approach for reliably detecting identification (ID) information from depolarized signals. Also, we report the first instance of describing the impact of surface permittivity variations, tag deformations, tilt angles, and read ranges, all integrated into model training for enhanced robustness in detecting ID information. The developed models facilitate real-time identification and recording of objects, enhancing IoT applications in varied environments. It was observed that both models were able to generalize well to given data, with Model-1 achieving a low RMSE of 0.040 (0.66%) on an unseen test dataset. However, the hybrid model reduced the error further by 27.5% with a test RMSE of 0.029 (0.48%).","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"768-778"},"PeriodicalIF":3.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11157779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the Influence of Liquid Level Height in Containers on RFID System Performance","authors":"Lei Zuo;Bihang Lei;Lingshuo Li;Bing Li;Baiqiang Yin;Lifen Yuan","doi":"10.1109/JRFID.2025.3605595","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3605595","url":null,"abstract":"Focusing on the issue of how variations in liquid level height within a container affect the performance of passive ultrahigh frequency (UHF) radio frequency identification (RFID) tags, this study derives a link budget model for a passive UHF RFID system based on RFID operational principles and electromagnetic wave propagation theory. Using power transmission coefficients, the study analyzes how impedance mismatch caused by liquid in the container affects system performance. To validate the theoretical model, a combination of simulations and indoor experiments was employed, establishing segmented models of the tag response signal power (RSSI) as a function of liquid level height in both vertical and horizontal tag orientations. The RSSI of two tags, Alien9662 and Alien9640, was tested in an open indoor environment across varying liquid levels from 0 mm to 140 mm, measuring signal strength variations under different liquid levels. Theoretical analysis and experimental results reveal that when the liquid level changes along the antenna’s bent arm, RSSI decreases significantly (e.g., from –43.4 dBm to –75.6 dBm for the Alien9662 tag in vertical deployment). when the liquid level changes along the small electrical loop, RSSI first increases and then decreases (e.g., from –52.8 dBm to –43.4 dBm for L < 20 mm), exhibiting a nonlinear variation with liquid level height. The RSSI changes observed in both tags align with the segmented models, validating the model’s accuracy. These findings not only provide a theoretical basis for understanding the impact of liquid environments on RFID system performance but also offer a reference for optimizing RFID tag placement in liquid containers, which could support practical applications such as inventory management and liquid level monitoring.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"758-767"},"PeriodicalIF":3.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Low-Complexity Structured Neural Network Approach to Intelligently Realize Wideband Multi-Beam Beamformers","authors":"Hansaka Aluvihare;Sivakumar Sivasankar;Xianqi Li;Arjuna Madanayake;Sirani M. Perera","doi":"10.1109/JRFID.2025.3602901","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3602901","url":null,"abstract":"True-time-delay (TTD) beamformers can produce wideband squint-free beams in both analog and digital signal domains, unlike frequency-dependent FFT beams. Our previous work showed that TTD beamformers can be efficiently realized using the elements of the delay Vandermonde matrix (DVM), answering the longstanding beam-squint problem. Thus, building on our work on DVM algorithms, we propose a structured neural network (StNN) to realize wideband multi-beam beamformers using structure-imposed weight matrices and submatrices. The structure and sparsity of the weight matrices and submatrices are shown to reduce the computational complexity of the NN significantly. The proposed StNN architecture has <inline-formula> <tex-math>$mathcal {O} boldsymbol {(p L M} log boldsymbol M)$ </tex-math></inline-formula> complexity compared to a conventional fully connected L-layers network with <inline-formula> <tex-math>$mathcal {O}(M^{2}L)$ </tex-math></inline-formula> complexity, where M is the number of nodes in each layer of the network, p is the number of sub-weight matrices per layer, and <inline-formula> <tex-math>$M gt gt p$ </tex-math></inline-formula>. We show numerical simulations in the 24 to 32 GHz range to demonstrate the numerical feasibility of realizing wideband multi-beam beamformers using the proposed StNN architecture. We also show the complexity reduction of the proposed NN and compare that with fully connected NNs, to show the efficiency of the proposed architecture without sacrificing accuracy. The accuracy of the proposed NN architecture was shown in terms of the mean squared error, which is based on an objective function of the weight matrices and beamformed signals of antenna arrays, while also normalizing nodes. The proposed StNN’s robustness was tested against channel impairments by simulating with Rayleigh fading at different signal-to-noise ratios (SNRs). We show that the proposed StNN architecture leads to a low-complexity NN to realize wideband multi-beam beamformers, enabling a path for reconfigurable intelligent systems.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"727-738"},"PeriodicalIF":3.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing Heterogeneous Network (HetNet) Communications for Wildfire Management: Mitigating the Effects of Adversarial and Environmental Threats","authors":"Nesrine Benchoubane;Olfa Ben Yahia;William Ferguson;Gürkan Gür;Sumit Chakravarty;Gregory Falco;Gunes Karabulut Kurt","doi":"10.1109/JRFID.2025.3601843","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3601843","url":null,"abstract":"In the face of adverse environmental conditions and cyber threats, robust communication systems for critical applications such as wildfire management and detection demand secure and resilient architectures. This paper presents a novel framework that considers both adversarial factors, building resilience into a heterogeneous network (HetNet)integrating Low Earth Orbit (LEO) satellite constellation with High-Altitude Platform Ground Stations (HAPGS) and Low-Altitude Platforms (LAPS), tailored to support wildfire management operations. Building upon our previous work on secure-by-component approach for link segment security, we extend protection to the communication layer by securing both Radio Frequency (RF)/Free Space Optics (FSO) management and different links. Through a case study, we quantify how environmental stressors impact secrecy capacity and expose the system to passive adversaries. Key findings demonstrate that atmospheric attenuation and beam misalignment can notably degrade secrecy capacity across both short- and long-range communication links, while high-altitude eavesdroppers face less signal degradation, increasing their interception capability. Moreover, increasing transmit power to counter environmental losses can inadvertently improve eavesdropper reception, thereby reducing overall link confidentiality. Our worknot only highlights the importance of protecting networks from these dual threats but also aligns with the IEEE P3536 Standard for Space System Cybersecurity Design, ensuring resilience and the prevention of mission failures.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"713-726"},"PeriodicalIF":3.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power Efficient Range Extension Techniques for Cattle Health Monitoring Application","authors":"Radhika Raina;Kamal Jeet Singh;Suman Kumar","doi":"10.1109/JRFID.2025.3600422","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3600422","url":null,"abstract":"Monitoring cattle behavior regularly is essential for early detection of illness, stress or unusual activity. Although many cattle health monitoring systems exist in the literature, they often overlook techniques that balance power efficiency with range extension. Thus, this paper proposes Bluetooth Low Energy (BLE) based power efficient range extension techniques. These methods include designing high gain antennas for both the transmitter and receiver, using retransmissions and integrating a Power Amplifier (PA) at the transmitter and a Low Noise Amplifier (LNA) at the receiver. By optimizing the PA’s transmission power and utilizing an LNA, the system achieves a communication range of upto approximately 2.5 km while conserving power. Moreover, a key novelty of this work is the smart power control mechanism that fine tunes the PA’s output at the end node, providing an effective balance between the extended range and reduced power usage- an area that has been largely overlooked in existing BLE based cattle monitoring solutions.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"669-681"},"PeriodicalIF":3.4,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Neuromorphic Coding for Distributed Wireless Spiking Neural Networks Based on Mutual Information and Energy Efficiency","authors":"Pietro Savazzi;Anna Vizziello;Fabio Dell’Acqua","doi":"10.1109/JRFID.2025.3600048","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3600048","url":null,"abstract":"Wireless spiking neural networks (WSNNs) enable energy-efficient communication, particularly beneficial for edge intelligence and learning within both terrestrial systems and Earth-space network configurations (beyond 5G/6G). Recent studies have highlighted that distributed wireless SNNs (DWSNNs) perform well in inference accuracy and energy-efficient operation in edge devices, despite the challenges posed by constrained bandwidth and spike loss probability. This makes the technology appealing for wireless sensor networks (WSNs) in space scenarios, where energy limitations are significant. In this paper, we explore neuromorphic impulse radio (IR) transmission methodologies tailored for DWSNNs, investigating various coding algorithms that implement IR modulations. Our assessment employs information-theoretic measures to evaluate performance in terms of transmission efficiency. Moreover, the different neuromorphic coding techniques will be evaluated by considering the energy consumption of edge devices under the same constraints of limited bandwidth and additive white Gaussian noise (AWGN), in order to highlight possible trade-offs between transmission and edge inference requirements.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"658-668"},"PeriodicalIF":3.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerodynamic Antenna Array for 5.8 GHz UAV Wireless Power Applications","authors":"Vinicius Uchoa Oliveira;Ricardo A. M. Pereira;Amit Kumar Baghel;Nuno B. Carvalho","doi":"10.1109/JRFID.2025.3599976","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3599976","url":null,"abstract":"Wireless power transfer (WPT) has the potential to supply energy to various applications, such as electric vehicles and uncrewed aerial vehicles (UAVs), enabling extended operation without direct physical connections. This article presents the design, simulation, and experimental validation of a patch antenna array optimized for RF power reception in UAVs, based on a traditional antenna array. To improve aerodynamic performance, structural modifications, such as holes and slits, were introduced to facilitate airflow while maintaining the electromagnetic integrity of the antenna. This new antenna was manufactured and evaluated in an anechoic chamber, achieving a measured gain of 16.6 dBi, closely matching the simulated 17.74 dBi for a <inline-formula> <tex-math>$4{times }4$ </tex-math></inline-formula> patch array. Additionally, computer fluid dynamics simulations were performed and the stream trace and drag coefficients were compared for both antennas, confirming that the design reduces drag and enhances stability, making it a viable solution for UAV applications.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"705-712"},"PeriodicalIF":3.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11129109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Robotic RFID-Localization","authors":"Dimitrios Kapsos;Athanasios Konstantis;Stavroula Siachalou;Aggelos Bletsas;Antonis G. Dimitriou","doi":"10.1109/JRFID.2025.3598860","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3598860","url":null,"abstract":"This paper presents different deep learning architectures that successfully solve the problem of localization of RFID tags by a single antenna on top of a robot in 2D space. Phase measurements, collected by an RFID reader on top of a moving robot, combined with the corresponding antenna-positions, are properly structured, as proposed herein, to form the input vector of different Multilayer Machine Learning Networks. The proposed architectures are originally tested in simulated data, suffering by zero-mean Gaussian noise, achieving centimeter-level accuracy, verifying the soundness of the proposed approach. Subsequently, the models are tested on experimental data involving hundreds of RFID tags and experiments, dividing the dataset into two disjoint sets, the training set and the test set. The proposed deep learning solutions outperformed a maximum-likelihood estimator, since the latter assumes only the effects of the Line-Of-Sight link, while Neural Networks (NNs) identify patterns resulting from all contributions. To the best of our knowledge, this is the first paper that proposes a way to restructure phase measurements collected by a moving robot in a manner that can then be solved by different Machine Learning architectures. The proposed methods provide a scalable and computationally efficient alternative for real-time RFID localization tasks, which can be expanded in 3D space.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"635-649"},"PeriodicalIF":3.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Massive MIMO Beam ID-Based Positioning Method With Low Earth Orbit Satellite Mega Constellations","authors":"Mahmoud Elsanhoury;Janne Koljonen;Fabricio S. Prol;Mohammed S. Elmusrati;Heidi Kuusniemi","doi":"10.1109/JRFID.2025.3598214","DOIUrl":"https://doi.org/10.1109/JRFID.2025.3598214","url":null,"abstract":"The growth of satellite-based positioning methods has revolutionized global navigation by providing reliable geolocation capabilities. However, traditional Global Navigation Satellite Systems (GNSS) are increasingly vulnerable to threats like jamming, spoofing, and interception, undermining their reliability in critical applications such as in-flight navigation and emergency services. To address these challenges, Low Earth Orbit (LEO) satellite constellations have emerged as a promising complement to GNSS infrastructure. LEO satellites, orbiting at lower altitudes with higher density, offer improved signal availability, reduced degradation, and better reception on Earth. This paper presents a LEO satellite-based positioning method via massive multiple-input multiple-output (mMIMO) beamforming antennas. The proposed technique not only mitigates GNSS vulnerabilities but also introduces a passive sensing mechanism that facilitates positioning without complex timing synchronization, improving resilience in jamming-prone environments. By utilizing LEO satellite beam identifiers as geographic pointers, our method enables precise positioning through LEO satellite ephemeris and beam pattern data. We validate this beam-based method through simulations, LEO constellation data, vehicular drive-test datasets, and probabilistic positioning models. Positioning results from the first dataset show a mean absolute error (MAE) of 9.15 meters and a 95th percentile error (p95%) of 19.07 meters when combining LEO satellite data with inertial motion data from a moving vehicle. Meanwhile, GNSS accuracy was MAE = 26.6 meters and p95% = 56.6 meters. The second dataset showed consistent results with accuracy improvements in MAE from 18.55 to 9.42 meters, RMSE from 22.24 to 12.05 meters, and p95% from 36.38 to 21.18 meters, compared to GNSS. These findings highlight the potential of LEO satellite positioning to improve accuracy and reliability in challenging environments, with implications for critical applications such as remote sensing, emergency response, search and rescue, and situational awareness.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"619-634"},"PeriodicalIF":3.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}