arXiv subjects

Jintao Wang

Publications and source records attributed to Jintao Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Dual-Qubit Hierarchical Fuzzy Neural Network for Image Classification: Enabling Relational Learning via Quantum Entanglement

Classical deep neural network models struggle to represent data uncertainty and capture dependencies between features simultaneously, especially under fuzzy or noisy conditions. Although a quantum-assisted hierarchical fuzzy neural network (QA-HFNN) was proposed to learn fuzzy membership for each feature, it cannot model dependencies between features due to its single-qubit encoding. To address this, this paper proposes a dual-qubit hierarchical fuzzy neural network (DQ-HFNN), encoding feature pairs onto a pair of entangled qubits, which extends the single-feature fuzzy model to a joint fuzzy representation. By introducing quantum entanglement, the dual-qubit circuit can encode non-classical correlations, enabling the model to directly learn relationship patterns between feature pairs. Experiments on benchmarks show that DQ-HFNN demonstrates higher classification accuracy than QA-HFNN, as well as classical deep learning baselines. Furthermore, ablation studies after controlling for circuit depth and parameter counts show that the performance gain mainly stems from the relational modeling capability enabled by entanglement rather than enhanced expressivity. The proposed DQ-HFNN model exhibits high parameter efficiency and fast inference speed. Experiments under noisy conditions suggest that it is robust against noise and has the potential to be implemented on noisy intermediate-scale quantum devices.

quant-ph↗

arXiv2025

Channel-Aware Multi-Domain Feature Extraction for Automatic Modulation Recognition in MIMO Systems

Automatic modulation recognition (AMR) is a key technology in non-cooperative communication systems, aiming to identify the modulation scheme from signals without prior information. Deep learning (DL)-based methods have gained wide attention due to their excellent performance, but research mainly focuses on single-input single-output (SISO) systems, with limited exploration for multiple-input multiple-output (MIMO) systems. The confounding effects of multi-antenna channels can interfere with the statistical properties of MIMO signals, making identification particularly challenging. To overcome these limitations, we propose a Channel-Aware Multi-Domain feature extraction (CAMD) framework for AMR in MIMO systems. Our CAMD framework reconstructs the transmitted signal through an efficient channel compensation module and achieves a more robust representation capability against channel interference by extracting and integrating multi-domain features, including intra-antenna temporal correlations and inter-antenna channel correlations. We have verified our method on the widely-used dataset, MIMOSig-Ref, with complex mobile channel environments. Extensive experiments confirm the performance advantages of CAMD over previous state-of-the-art methods.

eess.SP↗

arXiv2025

Leveraging Channel Knowledge Map for Multi-User Hierarchical Beam Training Under Position Uncertainty

Channel knowledge map (CKM) emerges as a promising framework to acquire location-specific channel information without consuming wireless resources, creating new horizons for advanced wireless network design and optimization. Despite its potential, the practical application of CKM in beam training faces several challenges. On one hand, the user's precise location is typically unavailable prior to beam training, which limits the utility of CKM since its effectiveness relies heavily on accurate input of position data. On the other hand, the intricate interplay among CKM, real-time observations, and training strategies has not been thoroughly studied, leading to suboptimal performance and difficulties in practical implementation. In this paper, we present a framework for CKM-aided beam training that addresses these limitations. For single-user scenario, we propose a reward-motivated beam-potential hierarchical strategy which integrates partial position information and CKM. This strategy models the user equipment (UE) position uncertainty and formulates the hierarchical searching process as a pruned binary search tree. An optimal hierarchical searching strategy with minimal overhead is derived by evaluating the weights and rewards of potential codewords. Furthermore, a low-complexity two-layer lookahead scheme is designed to balance overhead and computational demands. For multi-user scenario, we develop a correlation-driven position-pruning training scheme, where sidelobe gains from inter-user interference are exploited to provide additional side information for overhead reduction, allowing all users to be simultaneously assigned their respective supportive beams. Simulations validate the superior performances of proposed approaches in advancing 6G beam training.

cs.IT↗

arXiv2025

BeamCKM: A Framework of Channel Knowledge Map Construction for Multi-Antenna Systems

The channel knowledge map (CKM) enables efficient construction of high-fidelity mapping between spatial environments and channel parameters via electromagnetic information analysis. Nevertheless, existing studies are largely confined to single-antenna systems, failing to offer dedicated guidance for multi-antenna communication scenarios. To address the inherent conflict between traditional real-value pathloss map and multi-degree-of-freedom (DoF) coherent beamforming in B5G/6G systems, this paper proposes a novel concept of BeamCKM and CKMTransUNet architecture. The CKMTransUNet approach combines a UNet backbone for multi-scale feature extraction with a vision transformer (ViT) module to capture global dependencies among encoded linear vectors, utilizing a composite loss function to characterize the beam propagation characteristics. Furthermore, based on the CKMTransUNet backbone, this paper presents a methodology named M3ChanNet. It leverages the multi-modal learning technique and cross-attention mechanisms to extract intrinsic side information from environmental profiles and real-time multi-beam observations, thereby further improving the map construction accuracy. Simulation results demonstrate that the proposed method consistently outperforms state-of-the-art (SOTA) interpolation methods and deep learning (DL) approaches, delivering superior performance even when environmental contours are inaccurate. For reproducibility, the code is publicly accessible at https://github.com/github-whh/BeamCKM.

eess.SP↗

arXiv2025

Asymptotic Performance Analysis of Fluid Antenna Systems: An Extreme Value Theory Perspective

Fluid antenna systems (FAS) allow dynamic reconfiguration to achieve superior diversity gains and reliability. To quantify the performance scaling of FAS with a large number of antenna ports, this paper leverages extreme value theory (EVT) to conduct an asymptotic analysis of the outage probability (OP) and ergodic capacity (EC). The analysis reveals that the OP decays approximately exponentially with the number of antenna ports. Moreover, we establish upper and lower bounds for the asymptotic EC, uncovering its double-logarithmic scaling law. Furthermore, we re-substantiate these scaling laws by exploiting the fact that the mode of the Gumbel distribution scales logarithmically. Besides, we theoretically prove that spatial correlation among antenna ports degrades both OP and EC. All analytical findings are conclusively validated by numerical results.

cs.IT↗

arXiv2025

Graph-Aware Temporal Encoder Based Service Migration and Resource Allocation in Satellite Networks

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically distributed users. On the other hand, the dynamic nature of satellite orbits necessitates effective service migration strategies to maintain service continuity and quality as the coverage areas of satellites evolve. We formulate this problem as a spatio-temporal Markov decision process, where satellites, ground users, and flight users are modeled as nodes in a time-varying graph. The node features incorporate queuing dynamics to characterize packet loss probabilities. To solve this problem, we propose a Graph-Aware Temporal Encoder (GATE) that jointly models spatial correlations and temporal dynamics. GATE uses a two-layer graph convolutional network to extract inter-satellite and user dependencies and a temporal convolutional network to capture their short-term evolution, producing unified spatio-temporal representations. The resulting spatial-temporal representations are passed into a Hybrid Proximal Policy Optimization (HPPO) framework. This framework features a multi-head actor that outputs both discrete service migration decisions and continuous resource allocation ratios, along with a critic for value estimation. We conduct extensive simulations involving both persistent and intermittent users distributed across real-world population centers.

cs.NI↗

arXiv2025

Learning from the Best, Differently: A Diversity-Driven Rethinking on Data Selection

High-quality pre-training data is crutial for large language models, where quality captures factual reliability and semantic value, and diversity ensures broad coverage and distributional heterogeneity. Existing approaches typically rely on single or multiple-dimensional score-based selection. However, directly selecting top-scored data often degrades performance, and sampling from a broader range is required to recover results. The above non-monotonicity between dataset scores and downstream benchmark results reveals a fundamental bias: score-based methods collapse correlated dimensions, causing top-scored data to appear high-quality while systematically overlooking diversity. We argue that ensuring diversity requires decomposing correlated metrics into orthogonal feature dimensions, from which the top-scored data can be directly selected. Therefore, we proposed the Orthogonal Diversity-Aware Selection (ODiS) algorithm, which preserves both quality and diversity during data selection. First, ODiS evaluates data from multiple dimensions, covering language quality, knowledge quality, and comprehension difficulty. The multi-dimensional scores are then decorrelated via Principal Component Analysis (PCA), yielding orthogonal evaluation dimensions. For each dimension, a Roberta-based scorer is trained to regress the data onto PCA-projected scores, enabling scalable inference on large corpora. Finally, ODiS constructs the training dataset by selecting top-scored data within each orthogonal dimension, thereby ensuring both quality and diversity. Empirical results show that ODiS-selected data exhibit less than 2\% inter-dimension overlap, confirming orthogonality between dimensions. More importantly, models trained with ODiS-selected data significantly outperform other baselines on downstream benchmarks, highlighting the necessity of orthogonal, diversity-aware data selection for LLMs.

cs.CL↗

arXiv2025

Model-Driven Deep Learning Enhanced Joint Beamforming and Mode Switching for RDARS-Aided MIMO Systems

Reconfigurable distributed antenna and reflecting surface (RDARS) is a promising architecture for future sixth-generation (6G) wireless networks. In particular, the dynamic working mode configuration for the RDARS-aided system brings an extra selection gain compared to the existing reconfigurable intelligent surface (RIS)-aided system and distributed antenna system (DAS). In this paper, we consider the RDARS-aided downlink multiple-input multiple-output (MIMO) system and aim to maximize the weighted sum rate (WSR) by jointly optimizing the beamforming matrices at the based station (BS) and RDARS, as well as mode switching matrix at RDARS. The optimization problem is challenging to be solved due to the non-convex objective function and mixed integer binary constraint. To this end, a penalty term-based weight minimum mean square error (PWM) algorithm is proposed by integrating the majorization-minimization (MM) and weight minimum mean square error (WMMSE) methods. To further escape the local optimum point in the PWM algorithm, a model-driven DL method is integrated into this algorithm, where the key variables related to the convergence of PWM algorithm are trained to accelerate the convergence speed and improve the system performance. Simulation results are provided to show that the PWM-based beamforming network (PWM-BFNet) can reduce the number of iterations by half and achieve performance improvements of 26.53% and 103.2% at the scenarios of high total transmit power and a large number of RDARS transmit elements (TEs), respectively.

eess.SP↗

arXiv2025

HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Disease

Accurate diagnosis of Alzheimer's disease (AD) requires effectively integrating multimodal data and clinical expertise. However, existing methods often struggle to fully utilize multimodal information and lack structured mechanisms to incorporate dynamic domain knowledge. To address these limitations, we propose HoloDx, a knowledge- and data-driven framework that enhances AD diagnosis by aligning domain knowledge with multimodal clinical data. HoloDx incorporates a knowledge injection module with a knowledge-aware gated cross-attention, allowing the model to dynamically integrate domain-specific insights from both large language models (LLMs) and clinical expertise. Also, a memory injection module with a designed prototypical memory attention enables the model to retain and retrieve subject-specific information, ensuring consistency in decision-making. By jointly leveraging these mechanisms, HoloDx enhances interpretability, improves robustness, and effectively aligns prior knowledge with current subject data. Evaluations on five AD datasets demonstrate that HoloDx outperforms state-of-the-art methods, achieving superior diagnostic accuracy and strong generalization across diverse cohorts. The source code will be released upon publication acceptance.

cs.CV↗

arXiv2025

CSIYOLO: An Intelligent CSI-based Scatter Sensing Framework for Integrated Sensing and Communication Systems

ISAC is regarded as a promising technology for next-generation communication systems, enabling simultaneous data transmission and target sensing. Among various tasks in ISAC, scatter sensing plays a crucial role in exploiting the full potential of ISAC and supporting applications such as autonomous driving and low-altitude economy. However, most existing methods rely on either waveform and hardware modifications or traditional signal processing schemes, leading to poor compatibility with current communication systems and limited sensing accuracy. To address these challenges, we propose CSIYOLO, a framework that performs scatter localization only using estimated CSI from a single base station-user equipment pair. This framework comprises two main components: anchor-based scatter parameter detection and CSI-based scatter localization. First, by formulating scatter parameter extraction as an image detection problem, we propose an anchor-based scatter parameter detection method inspired by You Only Look Once architectures. After that, a CSI-based localization algorithm is derived to determine scatter locations with extracted parameters. Moreover, to improve localization accuracy and implementation efficiency, we design an extendable network structure with task-oriented optimizations, enabling multi-scale anchor detection and better adaptation to CSI characteristics. A noise injection training strategy is further designed to enhance robustness against channel estimation errors. Since the proposed framework operates solely on estimated CSI without modifying waveforms or signal processing pipelines, it can be seamlessly integrated into existing communication systems as a plugin. Experiments show that our proposed method can significantly outperform existing methods in scatter localization accuracy with relatively low complexities under varying numbers of scatters and estimation errors.

eess.SP↗

arXiv2025

On the Characterization and Evaluation of Doppler Squint in Wideband ODDM Systems

The recently proposed orthogonal delay-Doppler division multiplexing (ODDM) modulation has been demonstrated to enjoy excellent reliability over doubly-dispersive channels. However, most of the prior analysis tends to ignore the interactive dispersion caused by the wideband property of ODDM signal, which possibly leads to performance degradation. To solve this problem, we investigate the input-output relation of ODDM systems considering the wideband effect, which is also known as the Doppler squint effect (DSE) in the literature. The extra delay-Doppler (DD) dispersion caused by the DSE is first explicitly explained by employing the time-variant frequency response of multipath channels. Its characterization is then derived for both reduced cyclic prefix (RCP) and zero padded (ZP)-based wideband ODDM systems, where the extra DD spread and more complicated power leakage outside the peak region are presented theoretically. Numerical results are finally provided to confirm the significance of DSE. The derivations in this paper are beneficial for developing accurate signal processing techniques in ODDM-based integrated sensing and communication systems.

eess.SP↗

arXiv2025

Dynamic SINR-Guided Iterative Interference Cancellation for ODDM Systems in Doubly Dispersive Channels

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently gained significant attention as a promising candidate to promote the communication reliability in high-mobility environments. Low complexity signal detection is one of the most significant challenges for ODDM over general physical channels, due to the large channel spreading caused by the fractional delay and Doppler shifts. In this paper, we investigate the low-complexity data detection for ODDM system by utilizing iterative interference cancellation. Based on the theoretical analysis of signal to interference plus noise ratio (SINR) during the iteration, a dynamic SINR-guided approach is proposed to provide a better initialization result. Specifically, we analyze the SINR of each time domain sample before initial estimate with consideration of off-grid delay and Doppler shifts. The iteration is then started from the multi-carrier symbol index which has the best SINR. The corresponding interference is then eliminated for other time domain samples while the SINR for symbol awaiting detection is also updated. Based on the updated SINR, the next multi-carrier symbol index is selected for the same processing until all data symbols have been initialized. Finally, we update the SINR synchronously until the end of the initialization. Simulation experiments indicate that our proposed algorithms demonstrate satisfying convergence and error performance while avoiding the huge complexity introduced by full linear minimum mean squared error (LMMSE) initialization.

econ.TH↗

arXiv2025

Data-Driven Modulation Optimization with LMMSE Equalization for Reliability Enhancement in Underwater Acoustic Communications

Ultra-reliable underwater acoustic (UWA) communications serve as one of the key enabling technologies for future space-air-ground-underwater integrated networks. However, the reliability of current UWA transmission is still insufficient since severe performance degradation occurs for conventional multicarrier systems in UWA channels with severe delay-scale spread. To solve this problem, we exploit learning-inspired approaches to optimize the modulation scheme under the assumption of linear minimum mean square error (LMMSE) equalization, where the discrete representation of waveforms is adopted by utilizing Nyquist filters. The optimization problem is first transferred into maximizing the fairness of estimation mean square error (MSE) for each data symbol since the total MSE is invariant considering the property of orthogonal modulation. The Siamese architecture is then adopted to obtain consistent optimization results across various channel conditions, which avoids the overhead of online feedback, cooperation, and deployment of neural networks and guarantees generalization. The overall scheme including the loss function, neural network structure, and training process is also investigated in depth in this paper. The excellent performance and robustness of the proposed modulation scheme are verified by carrying out the bit error rate test over various UWA channels with severe delay-scale spread.

eess.SP↗

arXiv2025

Beamforming-Codebook-Aware Channel Knowledge Map Construction for Multi-Antenna Systems

Channel knowledge map (CKM) has emerged as a crucial technology for next-generation communication, enabling the construction of high-fidelity mappings between spatial environments and channel parameters via electromagnetic information analysis. Traditional CKM construction methods like ray tracing are computationally intensive. Recent studies utilizing neural networks (NNs) have achieved efficient CKM generation with reduced computational complexity and real-time processing capabilities. Nevertheless, existing research predominantly focuses on single-antenna systems, failing to address the beamforming requirements inherent to MIMO configurations. Given that appropriate precoding vector selection in MIMO systems can substantially enhance user communication rates, this paper presents a TransUNet-based framework for constructing CKM, which effectively incorporates discrete Fourier transform (DFT) precoding vectors. The proposed architecture combines a UNet backbone for multiscale feature extraction with a Transformer module to capture global dependencies among encoded linear vectors. Experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) deep learning (DL) approaches, yielding a 17\% improvement in RMSE compared to RadioWNet. The code is publicly accessible at https://github.com/github-whh/TransUNet.

eess.SP↗

arXiv2025

Lightweight and Self-Evolving Channel Twinning: An Ensemble DMD-Assisted Approach

Traditional channel acquisition faces significant limitations due to ideal model assumptions and scalability challenges. A novel environment-aware paradigm, known as channel twinning, tackles these issues by constructing radio propagation environment semantics using a data-driven approach. In the spotlight of channel twinning technology, a radio map is recognized as an effective region-specific model for learning the spatial distribution of channel information. However, most studies focus on static channel map construction, with only a few collecting numerous channel samples and using deep learning for radio map prediction. In this paper, we develop a novel dynamic radio map twinning framework with a substantially small dataset. Specifically, we present an innovative approach that employs dynamic mode decomposition (DMD) to model the evolution of the dynamic channel gain map as a dynamical system. We first interpret dynamic channel gain maps as spatio-temporal video stream data. The coarse-grained and fine-grained evolving modes are extracted from the stream data using a new ensemble DMD (Ens-DMD) algorithm. To mitigate the impact of noisy data, we design a median-based threshold mask technique to filter the noise artifacts of the twin maps. With the proposed DMD-based radio map twinning framework, numerical results are provided to demonstrate the low-complexity reproduction and evolution of the channel gain maps. Furthermore, we consider four radio map twin performance metrics to confirm the superiority of our framework compared to the baselines.

eess.SP↗

arXiv2025

Reconfigurable Codebook-Based Beamforming for RDARS-Aided mmWave MU-MIMO Systems

Reconfigurable distributed antenna and reflecting surface (RDARS) is a new architecture for the sixth-generation (6G) millimeter wave (mmWave) communications. In RDARS-aided mmWave systems, the active and passive beamforming design and working mode configuration for reconfigurable elements are crucial for system performance. In this paper, we aim to maximize the weighted sum rate (WSR) in the RDARS-aided mmWave system. To take advantage of RDARS, we first design a reconfigurable codebook (RCB) in which the number and dimension of the codeword can be flexibly adjusted. Then, a low overhead beam training scheme based on hierarchical search is proposed. Accordingly, the active and passive beamforming for data transmission is designed to achieve the maximum WSR for both space-division multiple access (SDMA) and time-division multiple access (TDMA) schemes. For the TDMA scheme, the optimal number of RDARS transmit elements and the allocated power budget for WSR maximization are derived in closed form. Besides, the superiority of the RDARS is verified and the conditions under which RDARS outperforms RIS and DAS are given. For the SDMA scheme, we characterize the relationship between the number of RDARS connected elements and the user distribution, followed by the derivation of the optimal placement positions of the RDARS transmit elements. High-quality beamforming design solutions are derived to minimize the inter-user interference (IUI) at the base station and RDARS side respectively, which nearly leads to the maximal WSR. Finally, simulation results confirm our theoretical findings and the superiority of the proposed schemes.

eess.SP↗

arXiv2025

New high-dimensional generalizations of Nesbitt's inequality and relative applications

Two kinds of novel generalizations of Nesbitt's inequality are explored in various cases regarding dimensions and parameters in this article. Some other cases are also discussed elaborately by using the semiconcave-semiconvex theorem. The general inequalities are then employed to deduce some alternate inequalities and mathematical competition questions. At last, a relation about Hurwitz-Lerch zeta functions is obtained.

math.GM↗

arXiv2025

Age of Synchronization Minimization in Wireless Networks with Random Updates and Time-Varying Timeliness Requirement

This study considers a wireless network where multiple nodes transmit status updates to a base station (BS) via a shared, error-free channel with limited bandwidth. The status updates arrive at each node randomly. We use the Age of Synchronization (AoS) as a metric to measure the information freshness of the updates. The AoS of each node has a timely-varying importance which follows a Markov chain. Our objective is to minimize the weighted sum AoS of the system. The optimization problem is relaxed and formulated as a constrained Markov decision process (CMDP). Solving the relaxed CMDP by a linear programming algorithm yields a stationary policy, which helps us propose a near-stationary policy for the original problem. Numerical simulations show that in most configurations, the AoS performance of our policy outperforms the policy choosing the maximum AoS regardless of weight variations.

cs.IT↗