arXiv subjects

Hongzhi Guo

Publications and source records attributed to Hongzhi Guo.

18 recordsLinked to original sources

arXiv2026

CADRE: Card-Agnostic Domain-Aligned RF Embeddings for Virtual PIN Pads on Passive NFC Cards

Near Field Communication (NFC) cards are widely used for identification, but their passive nature often limits the ability to incorporate additional security mechanisms. As a result, anyone holding the card may be incorrectly recognized as an authenticated user. To overcome this limitation, this paper presents a secure manual password input framework using a virtual PIN pad for passive NFC cards. Users input passwords by pressing designated regions on the card, which induces measurable impedance variations in the NFC antenna. These variations change the RF signals subtly, and a deep learning model is used to infer the intended password from the resulting signal patterns. A key challenge is that identical press interactions can produce significantly different responses across NFC cards, which yields unreliable recognition. To address this, we introduce a lightweight recognition approach that operates directly within the RF feature space at the penultimate layer of a temporal neural encoder. An adversarial domain-alignment module reshapes virtual PIN pad press-response embeddings into compact, card-invariant clusters, which enables stable and consistent recognition across heterogeneous cards. To support model training and evaluation, a reconfigurable software-defined radio (SDR) testbed is developed, and PIN pad press-response data are collected from commercially available ISO/IEC 15693 cards. Recognition is performed using a Mahalanobis distance metric derived from a calibration-based covariance model that captures feature correlations. Experimental results show that the proposed system achieves a 98.20\% recognition acceptance rate and remains robust under substantial noise degradation. The framework is fully card-agnostic and can be seamlessly integrated into existing NFC infrastructures.

eess.SP↗

arXiv2025

Sensing-Aware Transmit Waveform/Receive Filter Design for OFDM-MBS Systems

In this letter, we study the problem of cooperative sensing design for an orthogonal frequency division multiplexing (OFDM) multiple base stations (MBS) system. We consider a practical scenario where the base stations (BSs) exploit certain subcarriers to realize a sensing function. Since the high sidelobe level (SLL) of OFDM waveforms degrades radar detection for weak targets, and the cross-correlation generated by other BSs further exacerbates detection performance, we devise a joint design scheme for OFDM sequence and receive filter by minimizing the integrated sidelobe level (ISL) while satisfying mainlobe level, peak-to-average power ratio (PAPR) and spectrum allocation constraints. To address this non-convex problem, we propose an alternating optimization (AO)-based algorithm. Numerical simulations validate the effectiveness of the proposed method, demonstrating the superiority of SSL reduction in the MBS system over the matched filtering method.

eess.SP↗

arXiv2025

Task-Oriented Mulsemedia Communication using Unified Perceiver and Conformal Prediction in 6G Wireless Systems

The growing prominence of eXtended Reality (XR), holographic-type communications, and metaverse demands truly immersive user experiences by using many sensory modalities, including sight, hearing, touch, smell, taste, etc. Additionally, the widespread deployment of sensors in areas such as agriculture, manufacturing, and smart homes is generating diverse sensory data. A new media format known as multisensory media (mulsemedia) has emerged, which incorporates many sensory modalities beyond the traditional visual and auditory media. 6G wireless systems are envisioned to support the Internet of Senses, making it crucial to explore effective data fusion and communication strategies for mulsemedia. In this paper, we introduce a task-oriented multi-task mulsemedia communication system named MuSeCo, which is developed using unified Perceiver models and Conformal Prediction. This unified model can accept any sensory input and efficiently extract latent semantic features, making it adaptable for deployment across various Artificial Intelligence of Things (AIoT) devices. Conformal Prediction is employed for modality selection and combination, enhancing task accuracy while minimizing data communication overhead. The model is trained using six sensory modalities across four classification tasks. Simulations and experiments demonstrate that it can effectively fuse sensory modalities, significantly reduce end-to-end communication latency and energy consumption, and maintain high accuracy in communication-constrained systems.

eess.SP↗

arXiv2025

Model-Agnostic Uncertainty Quantification for Fast NFC Tag Identification using RF Fingerprinting

Near Field Communication (NFC) is widely used in security applications such as door access systems and ID cards. However, clone attacks can replicate digital information, enabling unauthorized access. RF fingerprinting offers a robust defense by extracting unique physical-layer features from NFC cards that cannot be cloned. While RF fingerprinting has been extensively applied to Internet of Things (IoT) device authentication, NFC tags present distinct characteristics that require specialized approaches. This paper focuses on RF fingerprinting for the ISO15693 NFC tag, which is a widely used international standard, by leveraging multi-channel, multi-rate data sampling to enhance accuracy. Deep learning and Random Forest models are employed to identify NFC tags, while uncertainty quantification, particularly Conformal Prediction, accelerates the identification process with high confidence and precision. A software-defined radio (SDR) testbed is developed to transmit customized commands and collect multi-channel multi-rate NFC signals. The multi-channel multi-rate NFC signals are progressively collected to ensure fast and accurate identification. Experimental results demonstrate that the proposed system achieves high accuracy by adaptively utilizing the optimal combination of NFC signals. The developed solution is model-agnostic which can be utilized for any machine learning-based NFC tag identification.

eess.SP↗

arXiv2025

TopoCode: Topologically Informed Error Detection and Correction in Communication Systems

Traditional error detection and correction codes focus on bit-level fidelity, which is insufficient for emerging technologies like eXtended Reality (XR) and holographic communications requiring high-data-rate, low-latency systems. Bit-level metrics cannot comprehensively evaluate Quality-of-Service (QoS) in these scenarios. This letter proposes TopoCode which leverages Topological Data Analysis (TDA) and persistent homology to encode topological information for message-level error detection and correction. It introduces minimal redundancy while enabling effective data reconstruction, especially in low Signal-to-Noise Ratio (SNR) conditions. TopoCode offers a promising approach to meet the demands of next-generation communication systems prioritizing semantic accuracy and message-level integrity.

cs.MM↗

arXiv2024

Battery-Free Sensor Array for Wireless Multi-Depth In-Situ Sensing

Underground in-situ sensing plays a vital role in precision agriculture and infrastructure monitoring. While existing sensing systems utilize wires to connect an array of sensors at various depths for spatial-temporal data collection, wireless underground sensor networks offer a cable-free alternative. However, these wireless sensors are typically battery-powered, necessitating periodic recharging or replacement. This paper proposes a battery-free sensor array which can be used for wireless multi-depth in-situ sensing. Utilizing Near Field Communication (NFC)-which can penetrate soil with negligible signal power loss-this sensor array can form a virtual magnetic waveguide, achieving long communication ranges. An analytical model has been developed to offer insights and determine optimal design parameters. Moreover, a prototype, constructed using off-the-shelf NFC sensors, was tested to validate the proposed concept. While this system is primarily designed for underground applications, it holds potential for other multi-depth in-situ sensing scenarios, including underwater environments.

eess.SP↗

arXiv2023

Mulsemedia Communication Research Challenges for Metaverse in 6G Wireless Systems

Although humans have five basic senses, sight, hearing, touch, smell, and taste, most multimedia systems in current systems only capture two of them, namely, sight and hearing. With the development of the metaverse and related technologies, there is a growing need for a more immersive media format that leverages all human senses. Multisensory media(Mulsemedia) that can stimulate multiple senses will play a critical role in the near future. This paper provides an overview of the history, background, use cases, existing research, devices, and standards of mulsemedia. Emerging mulsemedia technologies such as Extended Reality (XR) and Holographic-Type Communication (HTC) are introduced. Additionally, the challenges in mulsemedia research from the perspective of wireless communication and networking are discussed. The potential of 6G wireless systems to address these challenges is highlighted, and several research directions that can advance mulsemedia communications are identified.

cs.MM↗

arXiv2023

Mobility-Aware Computation Offloading for Swarm Robotics using Deep Reinforcement Learning

Swarm robotics is envisioned to automate a large number of dirty, dangerous, and dull tasks. Robots have limited energy, computation capability, and communication resources. Therefore, current swarm robotics have a small number of robots, which can only provide limited spatio-temporal information. In this paper, we propose to leverage the mobile edge computing to alleviate the computation burden. We develop an effective solution based on a mobility-aware deep reinforcement learning model at the edge server side for computing scheduling and resource. Our results show that the proposed approach can meet delay requirements and guarantee computation precision by using minimum robot energy.

cs.RO↗

arXiv2023

A Low-cost Through-metal Communication System for Sensors in Metallic Pipes

Metallic pipes and other containers are widely used to store and transport toxic gases and liquids. Various sensors have been designed to monitor the environment inside metallic pipes and containers, such as pressure, liquid-level, and chemical sensors. Moreover, sensors are also used to inspect and detect pipe leakages. However, sensors are usually placed outside of metallic pipes and containers and use ultrasound to monitor the internal unseen environment. This is mainly due to the fact that internal sensors cannot communicate with external data sinks without cables, but using cables can dramatically affect the metal-sealed structure. Wireless communication is desirable to communicate with internal sensors, but it experiences high attenuation losses since metal can block wireless signals due to its high conductivity. This paper develops a low-cost through-metal communication system prototype using off-the-shelf electronic components. The system is fully reconfigurable, and arbitrary modulation and coding schemes can be implemented. We design the transmit module which includes a signal processing microcontroller, an amplifier, and a transmit coil, and the receive module which includes a receive coil, an amplifier, and a microcontroller with demodulation algorithms and bit-error-rate (BER) calculations. The performance of the prototype is evaluated using various symbol rates, distances, and transmission power. The results show that the communication system can achieve a 500 bps data rate with 0.01 BER and 3.4 cm communication range when penetrating an Aluminum pipe with 7 mm thickness.

eess.SY↗

arXiv2020

MagBB: Wireless Charging for Batteryless Sensors using Magnetic Blind Beamforming

Tiny batteryless sensors are desirable since they create negligible impacts on the operation of the system being monitored or the surrounding environment. Wireless energy transfer for batteryless sensors is challenging since they cannot cooperate with the charger due to the lack of energy. In this paper, a Magnetic Blind Beamforming (MagBB) algorithm is developed for wireless energy transfer for batteryless sensors in inhomogeneous media. Batteryless sensors with randomly orientated coils may experience significant orientation losses and they may not receive any energy from the charger. MagBB uses a set of optimized current vectors to generate rotating magnetic fields which can ensure that coils on batteryless sensors with arbitrary orientations can receive sufficient voltages for charging. It does not require any information regarding the batteryless sensor's coil orientation or location. The efficiency of MagBB is proven by extensive numerical simulations.

eess.SP↗

arXiv2019

Envisioning Device-to-Device Communications in 6G

To fulfill the requirements of various emerging applications, the future sixth generation (6G) mobile network is expected to be an innately intelligent, highly dynamic, ultradense heterogeneous network that interconnects all things with extremely low-latency and high speed data transmission. It is believed that artificial intelligence (AI) will be the most innovative technique that can achieve intelligent automated network operations, management and maintenance in future complex 6G networks. Driven by AI techniques, device-to-device (D2D) communication will be one of the pieces of the 6G jigsaw puzzle. To construct an efficient implementation of intelligent D2D in future 6G, we outline a number of potential D2D solutions associating with 6G in terms of mobile edge computing, network slicing, and Non-orthogonal multiple access (NOMA) cognitive Networking.

cs.NI↗

arXiv2019

Reinforcement Learning-Enabled Reliable Wireless Sensor Networks in Dynamic Underground Environments

Wireless underground sensor networks play an important role in underground sensing such as climate-smart agriculture and underground infrastructure monitoring. Existing works consider a static underground environment, which is not practical since the dielectric parameters of soil change frequently due to precipitation and harsh weather. This challenge cannot be ignored in real implementation due to the drastic change of wireless underground channel. In this paper, we study the effect of dynamic underground environment on wireless communications for sensor networks. We use the real data collected by in-situ sensors to train a Hidden Markov Model. Then, by using reinforcement learning, we derive the optimal transmission policies for underground sensors to efficiently use their energy and reduce the number of dropped and unsuccessfully transmitted packets. Through simulations using real data, we find that the developed algorithm can reduce the packet loss and transmit the sensed data in a timely manner.

eess.SP↗

arXiv2019

Inter-Media Backscatter Communications with Magnetic Induction

Wireless sensors in extreme environments such as underground, concrete wall, and the human body, can enable a large number of important applications. However, deploying wireless sensors in such environments on a large scale is a great challenge due to the high cost and the large profile of wireless sensors. Backscatter communications can reduce the cost and size of wireless sensors by removing most of the typical wireless components. In this paper, we propose to leverage the RFID sensors for inter-media magnetic induction-based backscatter communications (MIBC). In this way, the complexity and cost of wireless sensors can be significantly reduced. The sensors leverage magnetic signals to backscatter information which demonstrate high penetration efficiency. We design a system with channel estimation and optimal signal transmission strategy, and an optimal receiver. The channel between the aboveground reader and underground sensors are modeled by using a stratified medium model. The bit-error-rate is evaluated with different configurations. The results suggest that MIBC can be utilized for most of the inter-media applications with low power consumption and high penetration efficiency.

eess.SP↗

arXiv2019

On Reliability of Underwater Magnetic Induction Communications with Tri-Axis Coils

Underwater magnetic induction communications (UWMICs) provide a low-power and high-throughput solution for autonomous underwater vehicles (AUVs), which are envisioned to explore and monitor the underwater environment. UWMIC with tri-axis coils increases the reliability of the wireless channel by exploring the coil orientation diversity. However, the UWMIC channel is different from typical fading channels and the mutual inductance information (MII) is not always available. It is not clear the performance of the tri-axis coil MIMO without MII. Also, its performances with multiple users have not been investigated. In this paper, we analyze the reliability and multiplexing gain of UWMICs with tri-axis coils by using coil selection. We optimally select the transmit and receive coils to reduce the computation complexity and power consumption and explore the diversity for multiple users. We find that without using all the coils and MII, we can still achieve reliability. Also, the multiplexing gain of UWMIC without MII is 5dB smaller than typical terrestrial fading channels. The results of this paper provide a more power-efficient way to use UWMICs with tri-axis coils.

eess.SP↗

arXiv2017

Practical Design and Implementation of Metamaterial-Enhanced Magnetic Induction Communication

Although wireless communications in complex environments, such as underground, underwater, and indoor, can enable a large number of novel applications, their performances are constrained by lossy media and complicated structures. Magnetic Induction (MI) has been proved to be an efficient solution to achieve reliable communication in such environments. However, due to the small coil antenna's physical limitation, MI's communication range is still very limited if devices are required to be portable. To this end, Metamaterial-enhanced Magnetic Induction (M$^2$I) communication has been proposed and the theoretical results predict that it can significantly increase the communication performance, namely, data rate and communication range. Nevertheless, currently, the real implementation of M$^2$I is still a challenge and there is no guideline on design and fabrication of spherical metamaterials. In this paper, a practical design is proposed by leveraging a spherical coil array to realize M$^2$I. We prove that the effectively negative permeability can be achieved and there exists a resonance condition where the radiated magnetic field can be significantly amplified. The radiation and communication performances are evaluated and full-wave simulation is conducted to validate the design objectives. By using the spherical coil array-based M$^2$I, the communication range can be significantly extended, exactly as we predicted in the ideal M$^2$I model. Finally, the proposed M$^2$I antenna is implemented and tested in various environments.

physics.class-ph↗

arXiv2017

Cooperative Raman Spectroscopy for Real-time In Vivo Nano-biosensing

In the last few decades, the development of miniature biological sensors that can detect and measure different phenomena at the nanoscale has led to transformative disease diagnosis and treatment techniques. Among others, biofunctional Raman nanoparticles have been utilized in vitro and in vivo for multiplexed diagnosis and detection of different biological agents. However, existing solutions require the use of bulky lasers to excite the nanoparticles and similarly bulky and expensive spectrometers to measure the scattered Raman signals, which limit the practicality and applications of this nano-biosensing technique. In addition, due to the high path loss of the intra-body environment, the received signals are usually very weak, which hampers the accuracy of the measurements. In this paper, the concept of cooperative Raman spectrum reconstruction for real-time in vivo nano-biosensing is presented for the first time. The fundamental idea is to replace the single excitation and measurement points (i.e., the laser and the spectrometer, respectively) by a network of interconnected nano-devices that can simultaneously excite and measure nano-biosensing particles. More specifically, in the proposed system a large number of nanosensors jointly and distributively collect the Raman response of nano-biofunctional nanoparticles (NBPs) traveling through the blood vessels. This paper presents a detailed description of the sensing system and, more importantly, proves its feasibility, by utilizing accurate models of optical signal propagation in intra-body environment and low-complexity estimation algorithms. The numerical results show that with a certain density of NBPs, the reconstructed Raman spectrum can be recovered and utilized to accurately extract the targeting intra-body information.

eess.SY↗

arXiv2016

M$^2$I: Channel Modeling for Metamaterial-Enhanced Magnetic Induction Communications

Magnetic Induction (MI) communication technique has shown great potentials in complex and RF-challenging environments, such as underground and underwater, due to its advantage over EM wave-based techniques in penetrating lossy medium. However, the transmission distance of MI techniques is limited since magnetic field attenuates very fast in the near field. To this end, this paper proposes Metamaterial-enhanced Magnetic Induction (M$^2$I) communication mechanism, where a MI coil antenna is enclosed by a metamaterial shell that can enhance the magnetic fields around the MI transceivers. As a result, the M$^2$I communication system can achieve tens of meters communication range by using pocket-sized antennas. In this paper, an analytical channel model is developed to explore the fundamentals of the M$^2$I mechanism, in the aspects of communication range and channel capacity, and the susceptibility to various hostile and complex environments. The theoretical model is validated through the finite element simulation software, Comsol Multiphysics. Proof-of-concept experiments are also conducted to validate the feasibility of M$^2$I.

cs.OH↗

arXiv2015

M$^2$I Communication: From Theoretical Modeling to Practical Design

Wireless communications in complex environments are constrained by lossy media and complicated structures. Magnetic Induction (MI) has been proved to be an efficient solution to extend the communication range. Due to the small coil antenna's physical limitation, however, MI's communication range is still very limited. To this end, Metamaterial-enhanced Magnetic Induction (M$^2$I) communication has been proposed and the theoretical results suggest that it can significantly increase the communication performance, namely, data rate and communication range. Nevertheless, currently, the real implementation of M$^2$I is still a challenge and there is no guideline on design and fabrication of spherical metamaterial. In this paper, we propose a practical design by using a spherical coil array to realize M$^2$I and we prove that it can achieve negative permeability and there exists a resonance condition where the radiated magnetic field can be significantly amplified. The radiation and communication performance are evaluated and full-wave simulation in COMSOL Multiphysics is conducted to validate the design objectives. By using the spherical coil array-based M$^2$I, the communication range can be significantly extended, exactly as we predicted in the theoretical model.

physics.ins-det↗