arXiv subjects

Jichen Zhang

Publications and source records attributed to Jichen Zhang.

17 recordsLinked to original sources

arXiv2026

Benchmarking Regularization Methods For 3D Radio Tomographic Imaging

Integrated Sensing and Communication (ISAC) is an important technology for 6G, enabling wireless systems to perceive their physical environment. Radio Tomographic Imaging (RTI) is a potential technique for device-free sensing in ISAC, reconstructing object locations and shapes from Received Signal Strength (RSS) measurements. It can complement other techniques, such as radar and LiDAR, by providing a narrowband modality with wavelength resolution. Recently, an extended version of RTI, known as the extended phaseless Rytov approximation (x3DPRA), has been developed to enhance RTI reconstruction quality, estimate material parameters, and extend RTI from two to three dimensions (3D). However, the 3D formulation is even more ill-posed than the 2D form, as the number of measurements is far fewer than the number of unknown voxels. In this work, we systematically evaluate and compare three distinct regularization approaches (Ridge, Total Variation, and Tensor Nuclear) within the x3DPRA framework. We provide a detailed performance analysis by evaluating both reconstruction quality and computational runtime across the three methods. Our findings show that Total Variation regularization provides the best reconstruction quality but not the fastest runtime, providing guidance for developing high-resolution 3D RTI systems for future ISAC applications.

eess.IV↗

arXiv2026

A Dual-Band Reconfigurable Shared-Aperture Antenna Array With Independent Sub-6-GHz and Centimeter-Wave Beam Control

A planar dual-band reconfigurable shared-aperture antenna array is proposed for compact next-generation wireless front ends that require both sub-6-GHz and centimeter-wave (cm-wave) coverage. The array integrates a 2 by 2 sub-6-GHz microstrip dipole array and a 4 by 4 cm-wave stacked patch array within the same aperture, while providing independent beam control in the two bands without conventional T/R modules or beamforming networks. Slot-coupled feeding is employed to separate the radiating aperture from the reconfigurable RF feeding networks and DC bias circuits. PIN-diode-loaded split feeding rings first provide independent 1-bit phase reconfigurability for both bands. A compact reconfigurable $90^{\circ}$ phase shifter is then introduced as an additional phase-control stage, resulting in 2-bit phase control for sub-6 GHz elements and cm-wave subarrays. To reduce cross-band coupling in the compact shared aperture, a double-layer electromagnetic band-gap (EBG) structure is used to suppress cm-wave surface waves and higher-order sub-6-GHz modes excited by the cm-wave elements. A prototype is fabricated and measured. In the sub-6-GHz band, 11 reconfigurable radiation patterns are obtained, including two difference patterns and nine directional beams, with a peak broadside gain of 10.5 dBi. In the cm-wave band, two-dimensional beam scanning up to $\pm40^{\circ}$ is demonstrated with a peak gain of 14.6 dBi in both the E-plane and H-plane. These results show that the proposed architecture can combine dual-band shared-aperture integration and independent reconfigurable beam control in a compact antenna platform.

eess.SP↗

arXiv2026

Stability Enhanced Gaussian Process Variational Autoencoders

A novel stability-enhanced Gaussian process variational autoencoder (SEGP-VAE) is proposed for indirectly training a low-dimensional linear time invariant (LTI) system, using high-dimensional video data. The mean and covariance function of the novel SEGP prior are derived from the definition of an LTI system, enabling the SEGP to capture the indirectly observed latent process using a combined probabilistic and interpretable physical model. The search space of LTI parameters is restricted to the set of semi-contracting systems via a complete and unconstrained parametrisation. As a result, the SEGP-VAE can be trained using unconstrained optimisation algorithms. Furthermore, this parametrisation prevents numerical issues caused by the presence of a non-Hurwitz state matrix. A case study applies SEGP-VAE to a dataset containing videos of spiralling particles. This highlights the benefits of the approach and the application-specific design choices that enabled accurate latent state predictions.

cs.LG↗

arXiv2025

Graph Convolutional Long Short-Term Memory Attention Network for Post-Stroke Compensatory Movement Detection Based on Skeleton Data

Most stroke patients experience upper limb motor dysfunction. Compensatory movements are prevalent during rehabilitation training, which is detrimental to patients' long-term recovery. Therefore, detecting compensatory movements is of great significance. In this study, a Graph Convolutional Long Short-Term Memory Attention Network (GCN-LSTM-ATT) based on skeleton data is proposed for the detection of compensatory movements after stroke. Sixteen stroke patients were selected in the research. The skeleton data of the patients performing specific rehabilitation movements were collected using the Kinect depth camera. After data processing, detection models were constructed respectively using the GCN-LSTM-ATT model, the Support Vector Machine(SVM), the K-Nearest Neighbor algorithm(KNN), and the Random Forest(RF). The results show that the detection accuracy of the GCN-LSTM-ATT model reaches 0.8580, which is significantly higher than that of traditional machine learning algorithms. Ablation experiments indicate that each component of the model contributes significantly to the performance improvement. These findings provide a more precise and powerful tool for the detection of compensatory movements after stroke, and are expected to facilitate the optimization of rehabilitation training strategies for stroke patients.

cs.CV↗

arXiv2025

Pixel-based Reconfigurable Beamforming Networks Emulating Physical Movement in FAS

The concept of Fluid Antenna Systems (FAS) has emerged as an attractive new system technology for use in sixth-generation (6G) wireless systems. However, most FAS implementations rely on mechanical antenna movement and thus are too slow to be useful. In this paper, a novel pixel-based reconfigurable beamforming network (PRBFN) is used to emulate movement in Fluid Antenna Systems (FASs). Using the insight that changing an antenna's physical position is equivalent to changing radiation patterns that satisfy the desired pattern correlation, the PRBFN is used to control the excitation current vectors of a multi-port antenna, thereby governing the pattern correlation. Key novelties of our work involve the selection of current vectors, and the methodology for scaling the PRBFN to realize large-aperture FAS. Results are provided for our PRBFN combined with an FAS (denoted as a PRBFN-FAS) when the equivalent physical movement is set to 1.5 wavelengths. Measurements demonstrate that the PRBFN-FAS provides the desired spatial correlation, including the Bessel function relation from Clarke's model across a 5\% bandwidth, satisfying FAS requirements. System-level experiments confirm the viability of the PRBFN-FAS in communication scenarios.

eess.SP↗

arXiv2025

Multiport Analytical Pixel Electromagnetic Simulator (MAPES) for AI-assisted RFIC and Microwave Circuit Design

This paper proposes a novel analytical framework, denoted the Multiport Analytical Pixel Electromagnetic Simulator (MAPES). MAPES enables efficient and accurate prediction of the electromagnetic (EM) performance of arbitrary pixel-based microwave (MW) and RFIC structures. Unlike the Internal Multiport Method (IMPM), which optimizes only connecting elements within a fixed, gap-separated pixel skeleton, MAPES operates directly on the all-pixel presence/absence formulation used in recent MW/RFIC design. This is enabled by diagonal virtual pixels, an occupancy-to-load mapping, and a multi-layer/via port-level formulation that have no counterpart in IMPM. By introducing virtual pixels and diagonal virtual pixels and inserting virtual ports at critical positions, MAPES captures all horizontal, vertical, and diagonal electromagnetic couplings within a single multiport impedance matrix. Only a small set of full-wave simulations (typically about 1% of the datasets required by AI-assisted EM emulators) is needed to construct this matrix. Subsequently, any arbitrary pixel configuration can be evaluated analytically using a closed-form multiport relation without additional full-wave calculations. The proposed approach eliminates data-driven overfitting and ensures accurate results across all design variations. Using MAPES, comprehensive examples for single- and double-layer PCBs and CMOS processes (180 nm and 65 nm) confirm that high prediction accuracy with 600-2000$\times$ speed improvement is achieved compared to CST simulations. Owing to its efficiency, scalability, and reliability, MAPES provides a practical and versatile tool for AI-assisted MW circuit and RFIC design across diverse fabrication technologies.

eess.SP↗

arXiv2025

Knowledge Reasoning Language Model: Unifying Knowledge and Language for Inductive Knowledge Graph Reasoning

Inductive Knowledge Graph Reasoning (KGR) aims to discover facts in open-domain KGs containing unknown entities and relations, which poses a challenge for KGR models in comprehending uncertain KG components. Existing studies have proposed Knowledge Graph Foundation Models (KGFMs) that learn structural invariances across KGs to handle this uncertainty. Recently, Large Language Models (LLMs) have demonstrated strong capabilities for open-domain knowledge reasoning. As a result, the latest research has focused on LLM-based KGFMs that integrate LLM knowledge with KG context for inductive KGR. However, the intrinsic knowledge of LLMs may be overshadowed by sparse KG context, leading to LLM knowledge distortion, which can cause irreversible damage to model reasoning. Moreover, existing LLM-based KGR methods still struggle to fully constrain generative hallucinations in LLMs, severely limiting the credibility of reasoning results. To address these limitations, we propose a Knowledge Reasoning Language Model (KRLM) that achieves unified coordination between LLM knowledge and KG context throughout the KGR process. Specifically, we design a Knowledge Reasoning Language (KRL) instruction format and a KRL tokenizer to align LLM knowledge with KG representations. Then, we propose a KRL attention layer that coordinates intrinsic LLM knowledge with additional KG context through a dynamic knowledge memory mechanism. Finally, a structure-aware next-entity predictor is proposed, which strictly constrains the reasoning results within a trustworthy knowledge domain. Extensive experimental results on 25 real-world inductive KGR datasets demonstrate the significant superiority of the proposed KRLM\footnote{Our source codes are available at https://anonymous.4open.science/r/KRLM-EA36 in both zero-shot reasoning and fine-tuning scenarios.

cs.CL↗

arXiv2025

Channel Estimation and Analog Precoding for Pixel-based Fluid-Antenna-Assisted Multiuser MIMO-OFDM Systems

Pixel-based fluid antennas provide enhanced multiplexing gains and quicker radiation pattern switching than traditional designs. However, this innovation introduces challenges for channel estimation and analog precoding due to the state-non-separable channel response problem. This paper explores a multiuser MIMO-OFDM system utilizing pixel-based fluid antennas, informed by measurements from a real-world prototype. We present a sparse channel recovery framework for uplink channel sounding, employing an approximate separable channel response model with DNN-based antenna radiation functions. We then propose two low-complexity channel estimation algorithms that leverage orthogonal matching pursuit and variational Bayesian inference to accurately recover channel responses across various scattering cluster angles. These estimations enable the prediction of composite channels for all fluid antenna states, leading to an analog precoding scheme that optimally selects switching states for different antennas. Our simulation results indicate that the proposed approach significantly outperforms several baseline methods, especially in high signal-to-noise ratio environments with numerous users.

eess.SP↗

arXiv2025

Constrained Diffusers for Safe Planning and Control

Diffusion models have shown remarkable potential in planning and control tasks due to their ability to represent multimodal distributions over actions and trajectories. However, ensuring safety under constraints remains a critical challenge for diffusion models. This paper proposes Constrained Diffusers, a novel framework that incorporates constraints into pre-trained diffusion models without retraining or architectural modifications. Inspired by constrained optimization, we apply a constrained Langevin sampling mechanism for the reverse diffusion process that jointly optimizes the trajectory and realizes constraint satisfaction through three iterative algorithms: projected method, primal-dual method and augmented Lagrangian approaches. In addition, we incorporate discrete control barrier functions as constraints for constrained diffusers to guarantee safety in online implementation. Experiments in Maze2D, locomotion, and pybullet ball running tasks demonstrate that our proposed methods achieve constraint satisfaction with less computation time, and are competitive to existing methods in environments with static and time-varying constraints.

eess.SY↗

arXiv2025

A Hybrid Transmitting and Reflecting Beyond Diagonal Reconfigurable Intelligent Surface with Independent Beam Control and Power Splitting

A hybrid transmitting and reflecting beyond diagonal reconfigurable intelligent surface (BD-RIS) design is proposed. Operating in the same aperture, frequency band and polarization, the proposed BD-RIS features independent beam steering control of its reflected and transmitted waves. In addition it provides a hybrid mode with both reflected and transmitted waves using tunable power splitting between beams. The BD-RIS comprises two phase reconfigurable antenna arrays interconnected by an array of tunable two-port power splitters. The two-port power splitter in each BD-RIS cell is built upon a varactor in parallel with a bias inductor to exert tunable impedance variations on transmission lines. Provided with variable reverse DC voltages, the two-port power splitter can control the power ratio of S11 over S21 from -20 dB to 20 dB, thus allowing tunable power splitting. Each antenna is 2-bit phase reconfigurable with 200 MHz bandwidth at 2.4 GHz so that each cell of BD-RIS can also achieve independent reflection and transmission phase control. To characterize and optimize the electromagnetic response of the proposed BD-RIS design, a Th\'evenin equivalent model and corresponding analytical method is provided. A BD-RIS with 4 by 4 cells was also prototyped and tested. Experiments show that in reflection and transmission mode, the fabricated BD-RIS can realize beam steering in reflection and transmission space, respectively. It is also verified that when operating in hybrid mode, the BD-RIS enables independent beam steering of the reflected and transmitted waves. This work helps fill the gap between realizing practical hardware design and establishing an accurate physical model for the hybrid transmitting and reflecting BD-RIS, enabling hybrid transmitting and reflecting BD-RIS assisted wireless communications.

eess.SP↗

arXiv2024

A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs

The advent of the sixth-generation (6G) networks presents another round of revolution for the mobile communication landscape, promising an immersive experience, robust reliability, minimal latency, extreme connectivity, ubiquitous coverage, and capabilities beyond communication, including intelligence and sensing. To achieve these ambitious goals, it is apparent that 6G networks need to incorporate the state-of-the-art technologies. One of the technologies that has garnered rising interest is fluid antenna system (FAS) which represents any software-controllable fluidic, conductive, or dielectric structure capable of dynamically changing its shape and position to reconfigure essential radio-frequency (RF) characteristics. Compared to traditional antenna systems (TASs) with fixed-position radiating elements, the core idea of FAS revolves around the unique flexibility of reconfiguring the radiating elements within a given space. One recent driver of FAS is the recognition of its position-flexibility as a new degree of freedom (dof) to harness diversity and multiplexing gains. In this paper, we provide a comprehensive tutorial, covering channel modeling, signal processing and estimation methods, information-theoretic insights, new multiple access techniques, and hardware designs. Moreover, we delineate the challenges of FAS and explore the potential of using FAS to improve the performance of other contemporary technologies. By providing insights and guidance, this tutorial paper serves to inspire researchers to explore new horizons and fully unleash the potential of FAS.

eess.SP↗

arXiv2024

A Pixel-based Reconfigurable Antenna Design for Fluid Antenna Systems

Fluid Antenna Systems (FASs) have recently been proposed for enhancing the performance of wireless communication. Previous antenna designs to meet the requirements of FAS have been based on mechanically movable or liquid antennas and therefore have limited reconfiguration speeds. In this paper, we propose a design for a pixel-based reconfigurable antenna (PRA) that meets the requirements of FAS and the required switching speed. It can provide 12 FAS ports across 1/2 wavelength and consists of an E-slot patch antenna and an upper reconfigurable pixel layer with 6 RF switches. Simulation and experimental results from a prototype operating at 2.5 GHz demonstrate that the design can meet the requirements of FAS including port correlation with matched impedance.

eess.SP↗

arXiv2024

A Shared-Aperture Dual-Band sub-6 GHz and mmWave Reconfigurable Intelligent Surface With Independent Operation

A novel dual-band reconfigurable intelligent surface (DBI-RIS) design that combines the functionalities of millimeter-wave (mmWave) and sub-6 GHz bands within a single aperture is proposed. This design aims to bridge the gap between current single-band reconfigurable intelligent surfaces (RISs) and wireless systems utilizing sub-6 GHz and mmWave bands that require RIS with independently reconfigurable dual-band operation. The mmWave element is realized by a double-layer patch antenna loaded with 1-bit phase shifters, providing two reconfigurable states. An 8x8 mmWave element array is selectively interconnected using three RF switches to form a reconfigurable sub-6 GHz element at 3.5 GHz. A suspended electromagnetic band gap (EBG) structure is proposed to suppress surface waves and ensure sufficient geometric space for the phase shifter and control networks in the mmWave element. A low-cost planar spiral inductor (PSI) is carefully optimized to connect mmWave elements, enabling the sub-6 GHz function without affecting mmWave operation. Finally, prototypes of the DBI-RIS are fabricated, and experimental verification is conducted using two separate measurement testbeds. The fabricated sub-6 GHz RIS successfully achieves beam steering within the range of -35 to 35 degrees for DBI-RIS with 4x4 sub-6 GHz elements, while the mmWave RIS demonstrates beam steering between -30 to 30 degrees for DBI-RIS with 8x8 mmWave elements, and have good agreement with simulation results.

eess.SP↗

arXiv2023

Bi-level Mixed-Integer Nonlinear Optimization for Pelagic Island Microgrid Group Energy Management Considering Uncertainty

To realize the safe, economical and low-carbon operation of the pelagic island microgrid group, this paper develops a bi-level energy management framework in a joint energy-reserve market where the microgrid group (MG) operator and renewable and storage aggregators (RSA) are independent stakeholders with their own interests. In the upper level, MG operator determines the optimal transaction prices with aggregators to minimize MG operation cost while ensuring all safety constraints are satisfied under uncertainty. In the lower level, aggregators utilize vessels for batteries swapping and transmission among islands in addition to energy arbitrage by participating in energy and reserve market to maximize their own revenue. An upper bound tightening iterative algorithm is proposed for the formulated problem with nonlinear terms and integer variables in the lower level to improve the efficiency and reduce the gap between upper bound and lower bound compared with existing reformulation and decomposition algorithm. Case studies validate the effectiveness of the proposed approach and demonstrate its advantage of the proposed approach in terms of optimality and computation efficiency, compared with other methods.

math.OC↗

arXiv2023

Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Network

This paper proposes a multiagent based bi-level operation framework for the low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the aggregate load agents optimize the control signals for various types of loads to maximize the profits; in the lower level, the distribution network operator makes optimal dispatching decisions to minimize the operational costs and calculates the distribution locational marginal price and carbon intensity. The distributed flexible load agent has only incomplete information of the distribution network and cooperates with other agents using networked communication. Finally, the problem is formulated into a networked multi-agent constrained Markov decision process, which is solved using a safe reinforcement learning algorithm called consensus multi-agent constrained policy optimization considering the carbon emission allowance for each agent. Case studies with the IEEE 33-bus and 123-bus distribution network systems demonstrate the effectiveness of the proposed approach, in terms of satisfying the carbon emission constraint on demand side, ensuring the safe operation of the distribution network and preserving privacy of both sides.

eess.SY↗

arXiv2023

Using KCWI to Explore the Chemical Inhomogeneities and Evolution of J1044+0353

J1044+0353 is considered a local analog of the young galaxies that ionized the intergalactic medium at high-redshift due to its low mass, low metallicity, high specific star formation rate, and strong high-ionization emission lines. We use integral field spectroscopy to trace the propagation of the starburst across this small galaxy using Balmer emission- and absorption-line equivalent widths and find a post-starburst population (~ 15 - 20 Myr) roughly one kpc east of the much younger, compact starburst (~ 3 - 4 Myr). Using the direct electron temperature method to map the O/H abundance ratio, we find similar metallicity (1 to 3 sigma) between the starburst and post-starburst regions but with a significant dispersion of about 0.3 dex within the latter. We also map the Doppler shift and width of the strong emission lines. Over scales several times the size of the galaxy, we discover a velocity gradient parallel to the galaxy's minor axis. The steepest gradients (~ 30 $\mathrm{km \ s^{-1} \ kpc^{-1}}$) appear to emanate from the oldest stellar association. We identify the velocity gradient as an outflow viewed edge-on based on the increased line width and skew in a biconical region. We discuss how this outflow and the gas inflow necessary to trigger the starburst affect the chemical evolution of J1044+0353. We conclude that the stellar associations driving the galactic outflow are spatially offset from the youngest association, and a chemical evolution model with a metal-enriched wind requires a more realistic inflow rate than a homogeneous chemical evolution model.

astro-ph.GA↗

arXiv2022

A Confirmation of a Conjecture on the Feldman's Two-armed Bandit Problem

Myopic strategy is one of the most important strategies when studying bandit problems. In this paper, we consider the two-armed bandit problem proposed by Feldman. With general distributions and utility functions, we obtain a necessary and sufficient condition for the optimality of the myopic strategy. As an application, we could solve Nouiehed and Ross's conjecture for Bernoulli two-armed bandit problems that myopic strategy stochastically maximizes the number of wins.

math.ST↗