arXiv subjects

Ji Wang

Publications and source records attributed to Ji Wang.

At least 73 records · Page 4Linked to original sources

arXiv2025

Mode Switching-based STAR-RIS with Discrete Phase Shifters

The increasing demand for cost-effective, high-speed Internet of Things (IoT) applications in the coming sixth-generation (6G) networks has driven research toward maximizing spectral efficiency and simplifying hardware designs. In this context, we investigate the sum rate maximization problem for a mode-switching discrete-phase shifters simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided multi-antenna access point network, emphasizing hardware efficiency and reduced cost. A mixed-integer nonlinear optimization framework is formulated for joint optimization of the active beamforming matrix, user power allocation, and STAR-RIS phase shift vectors, including binary transmission/reflection amplitudes and discrete phase shifters. To solve the formulated problem, we employ a block coordinate descent method, dividing it into three subproblems tackled using difference-of-concave programming and combinatorial optimization techniques. Numerical results validate the effectiveness of the proposed joint optimization approach, consistently achieving superior sum rate performance compared to partial optimization methods, thereby underscoring its potential for efficient and scalable 6G IoT systems.

cs.IT↗

arXiv2025

Frequency-Selective Modeling and Analysis for OFDM-Integrated Wideband Pinching-Antenna Systems

This letter investigates the integration of pinching-antenna systems (PASS) with orthogonal frequency division multiplexing (OFDM) to ensure their compatibility and to explore the frequency-selective behavior inherent to PASS. First, an end-to-end channel model for OFDM PASS is proposed based on electromagnetic-compliant modeling of waveguides and coupled-mode theory, which includes frequency-dependent waveguide attenuation, dispersion and antenna coupling effect. Furthermore, a critical dependence of the OFDM cyclic prefix (CP) overhead on the proximity of the operating frequency to the waveguide cutoff is revealed. Moreover, the phase misalignment effect across subcarriers in OFDM PASS is derived for an approximate pinching antenna location strategy based on path loss minimization, which reveals the phase misalignment is exacerbated for wider bandwidths and larger array size. Numerical results show that: 1) frequency-selective effects in OFDM PASS lead to substantial variations in subcarrier achievable rates, highlighting the necessity of operating above the waveguide cutoff frequency for effective communications; 2) waveguide dispersion mandates considerable CP overhead when operating near the cutoff frequency, severely impacting the spectral efficiency of OFDM PASS; and 3) the gentle linear waveguide attenuation in a practical PASS significantly more advantageous than the severe logarithmic path loss characteristic of fixed-location antennas.

cs.IT↗

arXiv2025

Gains: Fine-grained Federated Domain Adaptation in Open Set

Conventional federated learning (FL) assumes a closed world with a fixed total number of clients. In contrast, new clients continuously join the FL process in real-world scenarios, introducing new knowledge. This raises two critical demands: detecting new knowledge, i.e., knowledge discovery, and integrating it into the global model, i.e., knowledge adaptation. Existing research focuses on coarse-grained knowledge discovery, and often sacrifices source domain performance and adaptation efficiency. To this end, we propose a fine-grained federated domain adaptation approach in open set (Gains). Gains splits the model into an encoder and a classifier, empirically revealing features extracted by the encoder are sensitive to domain shifts while classifier parameters are sensitive to class increments. Based on this, we develop fine-grained knowledge discovery and contribution-driven aggregation techniques to identify and incorporate new knowledge. Additionally, an anti-forgetting mechanism is designed to preserve source domain performance, ensuring balanced adaptation. Experimental results on multi-domain datasets across three typical data-shift scenarios demonstrate that Gains significantly outperforms other baselines in performance for both source-domain and target-domain clients. Code is available at: https://github.com/Zhong-Zhengyi/Gains.

cs.LG↗

arXiv2025

Learning Function-to-Function Mappings: A Fourier Neural Operator for Next-Generation MIMO Systems

Next-generation multiple-input multiple-output (MIMO) systems, characterized by extremely large-scale arrays, holographic surfaces, three-dimensional architectures, and flexible antennas, are poised to deliver unprecedented data rates, spectral efficiency and stability. However, these advancements introduce significant challenges for physical layer signal processing, stemming from complex near-field propagation, continuous aperture modeling, sub-wavelength antenna coupling effects, and dynamic channel conditions. Conventional model-based and deep learning approaches often struggle with the immense computational complexity and model inaccuracies inherent in these new regimes. This article proposes a Fourier neural operator (FNO) as a powerful and promising tool to address these challenges. The FNO learns function-to-function mappings between infinite-dimensional function spaces, making them exceptionally well-suited for modeling complex physical systems governed by partial differential equations based on electromagnetic wave propagation. We first present the fundamental principles of FNO, demonstrating its mesh-free nature and function-to-function ability to efficiently capture global dependencies in the Fourier domain. Furthermore, we explore a range of applications of FNO in physical-layer signal processing for next-generation MIMO systems. Representative case studies on channel modeling and estimation for novel MIMO architectures demonstrate the superior performance of FNO compared to state-of-the-art methods. Finally, we discuss open challenges and outline future research directions, positioning FNO as a promising technology for enabling the enormous potential of next-generation MIMO systems.

cs.IT↗

arXiv2025

Beyond the Strongest LLM: Multi-Turn Multi-Agent Orchestration vs. Single LLMs on Benchmarks

We study multi-turn multi-agent orchestration, where multiple large language model (LLM) agents interact over multiple turns by iteratively proposing answers or casting votes until reaching consensus. Using four LLMs (Gemini 2.5 Pro, GPT-5, Grok 4, and Claude Sonnet 4) on GPQA-Diamond, IFEval, and MuSR, we conduct two experiments: (i) benchmarking orchestration against single-LLM baselines; and (ii) ablations on GPQA-Diamond that vary whether agents see who authored answers and whether they can observe ongoing votes. Orchestration matches or exceeds the strongest single model and consistently outperforms the others. Analysis of best-achievable orchestration performance shows potential for further gains. The ablations show that revealing authorship increases self-voting and ties, and that showing ongoing votes amplifies herding, which speeds convergence but can sometimes yield premature consensus.

cs.AI↗

arXiv2025

PEPSI Investigation, Retrieval, and Atlas of Numerous Giant Atmospheres (PIRANGA). III. Composition and winds in the atmosphere of TOI-1518 b

Ultra-hot Jupiters (UHJs) orbit close to their host stars and experience extreme conditions, making them important laboratories to explore atmospheric composition and dynamics. Transmission spectroscopy is a useful tool to reveal chemical species and their vertical and longitudinal distribution in the atmosphere. We use transmission spectra from the PEPSI spectrograph on the Large Binocular Telescope to search for species and measure their time-resolved wind velocities in the atmosphere of TOI-1518 b. We detect Fe I at 7.8$σ$ and Fe II at 8.9$σ$, and tentatively detect Cr I at 4.4$σ$ and Ni I at 4.0$σ$. The time-resolved wind velocities of Fe I show a velocity pattern that is consistent with the velocity pattern of Fe II. TOI-1518 b joins a small sample of UHJs for which time-resolved wind velocities have been measured.

astro-ph.EP↗

arXiv2025

Output-Feedback Boundary Control of Thermally and Flow-Induced Vibrations in Slender Timoshenko Beams

This work is motivated by the engineering challenge of suppressing vibrations in turbine blades of aero engines, which often operate under extreme thermal conditions and high-Mach aerodynamic environments that give rise to complex vibration phenomena, commonly referred to as thermally-induced and flow-induced vibrations. Using Hamilton's variational principle, the system is modeled as a rotating slender Timoshenko beam under thermal and aerodynamic loads, described by a coupled system of 2*2 hyperbolic PIDEs, parabolic PDE, and ODEs, where the nonlocal terms exist in the hyperbolic PDE domain, and where the external disturbance (heat flux) flows into one boundary of the heat PDE. For the general form of such mixed systems, we present the state-feedback control design based on the PDE backstepping method, and then design an extended state observer for the unmeasurable distributed states and external disturbances using only available boundary measurements. In the resulting output-feedback closed-loop system, the state of the uncontrolled boundary, i.e., the furthest state from the control input, is proved to be exponentially convergent to zero, and all signals are proved to be uniformly ultimately bounded. Moreover, if the external disturbance vanishes, the exponential stability of the overall system is obtained. The proposed control design is validated on an aero-engine flexible blade under extreme thermal and aerodynamic conditions.

cs.RO↗

arXiv2025

Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence

Most existing Large Language Model (LLM)-based agent frameworks rely on centralized orchestration, incurring high deployment costs, rigid communication topologies, and limited adaptability. To address these challenges, we introduce Symphony, a decentralized multi-agent system which enables lightweight LLMs on consumer-grade GPUs to coordinate. Symphony introduces three key mechanisms: (1) a decentralized ledger that records capabilities, (2) a Beacon-selection protocol for dynamic task allocation, and (3) weighted result voting based on CoTs. This design forms a privacy-saving, scalable, and fault-tolerant orchestration with low overhead. Empirically, Symphony outperforms existing baselines on reasoning benchmarks, achieving substantial accuracy gains and demonstrating robustness across models of varying capacities.

cs.LG↗

arXiv2025

Two-Timescale Learning for Pilot-Free ISAC Systems

A pilot-free integrated sensing and communication (ISAC) system is investigated, in which phase-modulated continuous wave (PMCW) and non-orthogonal multiple access (NOMA) waveforms are co-designed to achieve simultaneous target sensing and data transmission. To enhance effective data throughput (i.e., Goodput) in PMCW-NOMA ISAC systems, we propose a deep learning-based receiver architecture, termed two-timescale Transformer (T3former), which leverages a Transformer architecture to perform joint channel estimation and multi-user signal detection without the need for dedicated pilot signals. By treating the deterministic structure of the PMCW waveform as an implicit pilot, the proposed T3former eliminates the overhead associated with traditional pilot-based methods. The proposed T3former processes the received PMCW-NOMA signals on two distinct timescales, where a fine-grained attention mechanism captures local features across the fast-time dimension, while a coarse-grained mechanism aggregates global spatio-temporal dependencies of the slow-time dimension. Numerical results demonstrate that the proposed T3former significantly outperforms traditional successive interference cancellation (SIC) receivers, which avoids inherent error propagation in SIC. Specifically, the proposed T3former achieves a substantially lower bit error rate and a higher Goodput, approaching the theoretical maximum capacity of a pilot-free system.

cs.IT↗

arXiv2025

When Vision-Language Model (VLM) Meets Beam Prediction: A Multimodal Contrastive Learning Framework

As the real propagation environment becomes in creasingly complex and dynamic, millimeter wave beam prediction faces huge challenges. However, the powerful cross modal representation capability of vision-language model (VLM) provides a promising approach. The traditional methods that rely on real-time channel state information (CSI) are computationally expensive and often fail to maintain accuracy in such environments. In this paper, we present a VLM-driven contrastive learning based multimodal beam prediction framework that integrates multimodal data via modality-specific encoders. To enforce cross-modal consistency, we adopt a contrastive pretraining strategy to align image and LiDAR features in the latent space. We use location information as text prompts and connect it to the text encoder to introduce language modality, which further improves cross-modal consistency. Experiments on the DeepSense-6G dataset show that our VLM backbone provides additional semantic grounding. Compared with existing methods, the overall distance-based accuracy score (DBA-Score) of 0.9016, corresponding to 1.46% average improvement.

eess.SP↗

arXiv2025

The watery atmosphere of HD~209458~b revealed by joint $K$- and $L$-band high-resolution spectroscopy

We present a joint analysis of high-resolution $K$- and $L$-band observations of the benchmark hot Jupiter \hdb\ from the Keck Planet Imager and Characterizer (KPIC). One half night of observations were obtained in each bandpass covering similar pre-eclipse phases. The two epochs were then jointly analyzed using our atmospheric retrieval pipeline based on \petit\ to constrain the atmospheric pressure-temperature profile and chemical composition. Consistent with recent results from \textit{JWST} observations at lower spectral resolution, we obtain an oxygen-rich composition for \hdb\ ($\rm C/O < 10^{-3}$ at 95\% confidence) and a lower limit on the volatile metallicity similar to the solar value ($\rm [(C+O)/H] > -0.2$ at 95\% confidence). Leveraging the large spectral grasp of the multi-band observations, we constrain the H$_2$O mixing ratio to $\rm \log H_2O_{VMR} > -3.1$ at 95\% confidence, and obtain 95\% upper limits on the atmospheric mixing ratios of CO ($<10^{-4.8}$), CH$_4$ ($<10^{-4.5}$), NH$_3$ ($<10^{-5.8}$), H$_2$S ($<10^{-3.3}$), and HCN ($<10^{-5.6}$). The limits on CH$_4$, NH$_3$, and HCN are consistent with recent results from \textit{JWST} transmission spectroscopy, demonstrating the value of multi-band ground-based high resolution spectroscopy for precisely constraining trace species abundances in exoplanet atmospheres. The retrieved low-C/O, moderate-metallicity composition for \hdb\ is consistent with formation scenarios involving late accretion of substantial quantities of oxygen-rich refractory solids and/or ices.

astro-ph.EP↗

arXiv2025

Fringing analysis and forward modeling of Keck Planet Imager and Characterizer (KPIC) spectra

The Keck Planet Imager and Characterizer (KPIC) combines high contrast imaging with high resolution spectroscopy (R~35,000 in K band) to study directly imaged exoplanets and brown dwarfs in unprecedented detail. KPIC aims to spectrally characterize substellar companions through measurements of planetary radial velocities, spins, and atmospheric composition. Currently, the dominant source of systematic noise for KPIC is fringing, or oscillations in the spectrum as a function of wavelength. The fringing signal can dominate residuals by up to 10% of the continuum for high S/N exposures, preventing accurate wavelength calibration, retrieval of atmospheric parameters, and detection of planets with flux ratios less than 1% of the host star. To combat contamination from fringing, we first identify its three unique sources and adopt a physically informed model of Fabry-Perot cavities to apply to post-processed data. We find this strategy can effectively model the fringing in observations of A0V/F0V stars, reducing the residual systematics caused by fringing by a factor of 2. Next, we wedge two of the transmissive optics internal to KPIC to eliminate two sources of fringing and confirm the third source as the entrance window to the spectrograph. Finally, we apply our previous model of the Fabry-Perot cavity to new data taken with the wedged optics to reduce the amplitude of the residuals by a factor of 10.

astro-ph.EP↗

arXiv2025

Mixed Precision Photonic Computing with 3D Electronic-Photonic Integrated Circuits

We propose advancing photonic in-memory computing through three-dimensional photonic-electronic integrated circuits using phase-change materials (PCM) and AlGaAs-CMOS technology. These circuits offer high precision (greater than 12 bits), scalability (greater than 1024 by 1024), and massive parallelism (greater than 1 million operations) across the wavelength, spatial, and temporal domains at ultra-low power (less than 1 watt per PetaOPS). Monolithically integrated hybrid PCM-AlGaAs memory resonators handle coarse-precision iterations (greater than 5-bit most significant bit precision) through reversible PCM phase transitions. Electro-optic memristive tuning enables fine-precision updates (greater than 8-bit least significant bit precision), resulting in over 12-bit precision for in-memory computing. The use of low-loss PCM (less than 0.01 dB per cm) and electro-optical tuning yields memristive optical resonators with high Q-factors (greater than 1 million), low insertion loss, and low tuning power. A W by W photonic tensor core composed of PCM-AlGaAs memresonators performs general matrix multiplication (GEMM) across W wavelengths from optical frequency combs, with minimal crosstalk and loss. Hierarchical scaling in the wavelength domain (K) and spatial domain (L) enables this system to address high-dimensional (N) scientific partial differential equation (PDE) problems in a single constant-time operation, compared to the conventional quadratic-time (N squared) computational complexity.

physics.optics↗

arXiv2025

Rotatable RIS Assisted Physical Layer Multicasting

Reconfigurable Intelligent Surfaces (RIS) dynamically control signal propagation to enhance wireless communications. This paper presents a novel framework for rotatable RIS assisted physical-layer multicast systems, aiming to maximize the sum of minimum multicast rates via joint optimization of base station beamforming, RIS phase shifts, and orientation. Unlike unicast or non-rotatable setups, the rotatable RIS adapts orientation to align signals with user groups, improving fairness and rates for weak users. An alternating optimization approach combines convex optimization for beamforming/phase shifts with exhaustive search and particle swarm optimization (PSO) for orientation. Majorization-Minimization-based algorithms solve subproblems iteratively. Simulation results show the framework achieves 24.1% rate improvement via exhaustive search and 20.0% via PSO over the non-rotatable RIS baseline, with PSO performance close to the exhaustive search upper bound, highlighting the benefits of physical-layer multicast and orientation optimization.

cs.IT↗

arXiv2025

Deep Learning Based Joint Channel Estimation and Positioning for Sparse XL-MIMO OFDM Systems

This paper investigates joint channel estimation and positioning in near-field sparse extra-large multiple-input multiple-output (XL-MIMO) orthogonal frequency division multiplexing (OFDM) systems. To achieve cooperative gains between channel estimation and positioning, we propose a deep learning-based two-stage framework comprising positioning and channel estimation. In the positioning stage, the user's coordinates are predicted and utilized in the channel estimation stage, thereby enhancing the accuracy of channel estimation. Within this framework, we propose a U-shaped Mamba architecture for channel estimation and positioning, termed as CP-Mamba. This network integrates the strengths of the Mamba model with the structural advantages of U-shaped convolutional networks, enabling effective capture of local spatial features and long-range temporal dependencies of the channel. Numerical simulation results demonstrate that the proposed two-stage approach with CP-Mamba architecture outperforms existing baseline methods. Moreover, sparse arrays (SA) exhibit significantly superior performance in both channel estimation and positioning accuracy compared to conventional compact arrays.

eess.SP↗

arXiv2025

Task-Specific Zero-shot Quantization-Aware Training for Object Detection

Quantization is a key technique to reduce network size and computational complexity by representing the network parameters with a lower precision. Traditional quantization methods rely on access to original training data, which is often restricted due to privacy concerns or security challenges. Zero-shot Quantization (ZSQ) addresses this by using synthetic data generated from pre-trained models, eliminating the need for real training data. Recently, ZSQ has been extended to object detection. However, existing methods use unlabeled task-agnostic synthetic images that lack the specific information required for object detection, leading to suboptimal performance. In this paper, we propose a novel task-specific ZSQ framework for object detection networks, which consists of two main stages. First, we introduce a bounding box and category sampling strategy to synthesize a task-specific calibration set from the pre-trained network, reconstructing object locations, sizes, and category distributions without any prior knowledge. Second, we integrate task-specific training into the knowledge distillation process to restore the performance of quantized detection networks. Extensive experiments conducted on the MS-COCO and Pascal VOC datasets demonstrate the efficiency and state-of-the-art performance of our method. Our code is publicly available at: https://github.com/DFQ-Dojo/dfq-toolkit .

cs.CV↗

arXiv2025

The impact of parameter spread of high-temperature superconducting Josephson junctions on the performance of quantum-based voltage standards

Quantum metrology based on Josephson junction array reproduces the most accurate desired voltage by far, therefore being introduced to provide voltage standards worldwide. In this work, we quantitatively analyzed the dependence of the first Shapiro step height of the junction array at 50 GHz on the parameter spread of 10,000 Josephson junctions by numerical simulation with resistively shunted junction model. The results indicate an upper limit spread of the critical current and resistance of the Josephson junctions. Specifically, to keep the maximum first Shapiro step above 0.88 mA, the critical current standard deviation, $σ$, should not exceed 25%, and for it to stay above 0.6 mA, the resistance standard deviation should not exceed 1.5%.

cond-mat.supr-con↗

arXiv2025

Resource Allocation for Pinching-Antenna Systems: State-of-the-Art, Key Techniques and Open Issues

Pinching antennas have emerged as a promising technology for reconfiguring wireless propagation environments, particularly in high-frequency communication systems operating in the millimeter-wave and terahertz bands. By enabling dynamic activation at arbitrary positions along a dielectric waveguide, pinching antennas offer unprecedented channel reconfigurability and the ability to provide line-of-sight (LoS) links in scenarios with severe LoS blockages. The performance of pinching-antenna systems is highly dependent on the optimized placement of the pinching antennas, which must be jointly considered with traditional resource allocation (RA) variables -- including transmission power, time slots, and subcarriers. The resulting joint RA problems are typically non-convex with complex variable coupling, necessitating sophisticated optimization techniques. This article provides a comprehensive survey of existing RA algorithms designed for pinching-antenna systems, supported by numerical case studies that demonstrate their potential performance gains. Key challenges and open research problems are also identified to guide future developments in this emerging field.

cs.IT↗