arXiv subjects

Zhu Han

Publications and source records attributed to Zhu Han.

At least 91 records · Page 5Linked to original sources

arXiv2025

Network-Centric Anomaly Filtering and Spoofer localization for 5G-NR Localization in LAWNs

This paper investigates security vulnerabilities and countermeasures for the 3rd Generation Partnership Project (3GPP) Fifth Generation New Radio (5G-NR) Time Difference of Arrival (TDoA)-based unmanned aerial vehicle (UAV) localization in low-altitude urban environments. We first optimize node selection strategies under Air to Ground (A2G) channel conditions, proving that optimal selection depends on UAV altitude and deployment density, and propose lightweight User Equipment (UE)-assisted approaches that reduce overhead while enhancing accuracy. Next, we then expose critical security vulnerabilities by introducing merged-peak spoofing attacks where rogue UAVs transmit multiple 5G-NR Positioning Reference Signalss (PRSs) that merge with legitimate signals, bypassing existing detection methods. Through theoretical modeling and sensitivity analysis, we quantify how synchronization quality and geometric factors determine spoofing success probability, thereby revealing fundamental weaknesses in current 3GPP positioning frameworks. To address these vulnerabilities, we design a network-centric anomaly detection framework at the Localization Management Function (LMF) using 3GPP-specified parameters, coupled with recursive gradient descent-based robust localization that filters anomalous data while estimating UAV position. Our unified framework simultaneously provides robust victim localization and spoofer localization, enabling active attacker attribution beyond passive defense. Extensive simulations validate the effectiveness of our optimization and security mechanisms for 3GPP-compliant UAV positioning.

eess.SP↗

arXiv2025

Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classification

Interpretability is essential in Whole Slide Image (WSI) analysis for computational pathology, where understanding model predictions helps build trust in AI-assisted diagnostics. While Integrated Gradients (IG) and related attribution methods have shown promise, applying them directly to WSIs introduces challenges due to their high-resolution nature. These methods capture model decision patterns but may overlook class-discriminative signals that are crucial for distinguishing between tumor subtypes. In this work, we introduce Contrastive Integrated Gradients (CIG), a novel attribution method that enhances interpretability by computing contrastive gradients in logit space. First, CIG highlights class-discriminative regions by comparing feature importance relative to a reference class, offering sharper differentiation between tumor and non-tumor areas. Second, CIG satisfies the axioms of integrated attribution, ensuring consistency and theoretical soundness. Third, we propose two attribution quality metrics, MIL-AIC and MIL-SIC, which measure how predictive information and model confidence evolve with access to salient regions, particularly under weak supervision. We validate CIG across three datasets spanning distinct cancer types: CAMELYON16 (breast cancer metastasis in lymph nodes), TCGA-RCC (renal cell carcinoma), and TCGA-Lung (lung cancer). Experimental results demonstrate that CIG yields more informative attributions both quantitatively, using MIL-AIC and MIL-SIC, and qualitatively, through visualizations that align closely with ground truth tumor regions, underscoring its potential for interpretable and trustworthy WSI-based diagnostics

cs.CV↗

arXiv2025

RIS-based Communication Enhancement and Location Privacy Protection in UAV Networks

With the explosive advancement of unmanned aerial vehicles (UAVs), the security of efficient UAV networks has become increasingly critical. Owing to the open nature of its communication environment, illegitimate malicious UAVs (MUs) can infer the position of the source UAV (SU) by analyzing received signals, thus compromising the SU location privacy. To protect the SU location privacy while ensuring efficient communication with legitimate receiving UAVs (RUs), we propose an Active Reconfigurable Intelligent Surface (ARIS)-assisted covert communication scheme based on virtual partitioning and artificial noise (AN). Specifically, we design a novel ARIS architecture integrated with an AN module. This architecture dynamically partitions its reflecting elements into multiple sub-regions: one subset is optimized to enhance the communication rate between the SU and RUs, while the other subset generates AN to interfere with the localization of the SU by MUs. We first derive the Cramér-Rao Lower Bound (CRLB) for the localization with received signal strength (RSS), based on which, we establish a joint optimization framework for communication enhancement and localization interference. Subsequently, we derive and validate the optimal ARIS partitioning and power allocation under average channel conditions. Finally, tailored optimization methods are proposed for the reflection precoding and AN design of the two partitions. Simulation results validate that, compared to baseline schemes, the proposed scheme significantly increases the localization error of the SU by MUs while maintaining efficient communication between the SU and RUs, thereby effectively protecting the SU location privacy.

eess.SY↗

arXiv2025

Beyond Gaussian Assumptions: A General Fractional HJB Control Framework for Lévy-Driven Heavy-Tailed Channels in 6G

Emerging 6G wireless systems suffer severe performance degradation in challenging environments like high-speed trains traversing dense urban corridors and Unmanned Aerial Vehicles (UAVs) links over mountainous terrain. These scenarios exhibit non-Gaussian, non-stationary channels with heavy-tailed fading and abrupt signal fluctuations. To address these challenges, this paper proposes a novel wireless channel model based on symmetric $α$-stable Lévy processes, thereby enabling continuous-time state-space characterization of both long-term and short-term fading. Building on this model, a generalized optimal control framework is developed via a fractional Hamilton-Jacobi-Bellman (HJB) equation that incorporates the Riesz fractional operator to capture non-local spatial effects and memory-dependent dynamics. The existence and uniqueness of viscosity solutions to the fractional HJB equation are rigorously established, thus ensuring the theoretical validity of the proposed control formulation. Numerical simulations conducted in a multi-cell, multi-user downlink setting demonstrate the effectiveness of the fractional HJB-based strategy in optimizing transmission power under heavy-tailed co-channel and multi-user interference.

eess.SY↗

arXiv2025

Strategic Decision-Making Under Uncertainty through Bi-Level Game Theory and Distributionally Robust Optimization

In strategic scenarios where decision-makers operate at different hierarchical levels, traditional optimization methods are often inadequate for handling uncertainties from incomplete information or unpredictable external factors. To fill this gap, we introduce a mathematical framework that integrates bi-level game theory with distributionally robust optimization (DRO), particularly suited for complex network systems. Our approach leverages the hierarchical structure of bi-level games to model leader-follower interactions while incorporating distributional robustness to guard against worst-case probability distributions. To ensure computational tractability, the Karush-Kuhn-Tucker (KKT) conditions are used to transform the bi-level challenge into a more manageable single-level model, and the infinite-dimensional DRO problem is reformulated into a finite equivalent. We propose a generalized algorithm to solve this integrated model. Simulation results validate our framework's efficacy, demonstrating that under high uncertainty, the proposed model achieves up to a 22\% cost reduction compared to traditional stochastic methods while maintaining a service level of over 90\%. This highlights its potential to significantly improve decision quality and robustness in networked systems such as transportation and communication networks.

eess.SY↗

arXiv2025

3D Gaussian Radiation Field Modeling for Integrated RIS-FAS Systems: Analysis and Optimization

The integration of reconfigurable intelligent surfaces (RIS) and fluid antenna systems (FAS) has attracted considerable attention due to its tremendous potential in enhancing wireless communication performance. However, under fast-fading channel conditions, rapidly and effectively performing joint optimization of the antenna positions in an FAS system and the RIS phase configuration remains a critical challenge. Traditional optimization methods typically rely on complex iterative computations, thus making it challenging to obtain optimal solutions in real time within dynamic channel environments. To address this issue, this paper introduces a field information-driven optimization method based on three-dimensional Gaussian radiation-field modeling for real-time optimization of integrated FAS-RIS systems. In the proposed approach, obstacles are treated as virtual transmitters and, by separately learning the amplitude and phase variations, the model can quickly generate high-precision channel information based on the transmitter's position. This design eliminates the need for extensive pilot overhead and cumbersome computations. On this framework, an alternating optimization scheme is presented to jointly optimize the FAS position and the RIS phase configuration. Simulation results demonstrate that the proposed method significantly outperforms existing approaches in terms of spectrum prediction accuracy, convergence speed, and minimum achievable rate, validating its effectiveness and practicality in fast-fading scenarios.

cs.NI↗

arXiv2025

Distributionally Robust Wireless Semantic Communication with Large AI Models

Semantic communication (SemCom) has emerged as a promising paradigm for 6G wireless systems by transmitting task-relevant information rather than raw bits, yet existing approaches remain vulnerable to dual sources of uncertainty: semantic misinterpretation arising from imperfect feature extraction and transmission-level perturbations from channel noise. Current deep learning based SemCom systems typically employ domain-specific architectures that lack robustness guarantees and fail to generalize across diverse noise conditions, adversarial attacks, and out-of-distribution data. In this paper, a novel and generalized semantic communication framework called WaSeCom is proposed to systematically address uncertainty and enhance robustness. In particular, Wasserstein distributionally robust optimization is employed to provide resilience against semantic misinterpretation and channel perturbations. A rigorous theoretical analysis is performed to establish the robust generalization guarantees of the proposed framework. Experimental results on image and text transmission demonstrate that WaSeCom achieves improved robustness under noise and adversarial perturbations. These results highlight its effectiveness in preserving semantic fidelity across varying wireless conditions.

cs.NI↗

arXiv2025

LAFA: Agentic LLM-Driven Federated Analytics over Decentralized Data Sources

Large Language Models (LLMs) have shown great promise in automating data analytics tasks by interpreting natural language queries and generating multi-operation execution plans. However, existing LLM-agent-based analytics frameworks operate under the assumption of centralized data access, offering little to no privacy protection. In contrast, federated analytics (FA) enables privacy-preserving computation across distributed data sources, but lacks support for natural language input and requires structured, machine-readable queries. In this work, we present LAFA, the first system that integrates LLM-agent-based data analytics with FA. LAFA introduces a hierarchical multi-agent architecture that accepts natural language queries and transforms them into optimized, executable FA workflows. A coarse-grained planner first decomposes complex queries into sub-queries, while a fine-grained planner maps each subquery into a Directed Acyclic Graph of FA operations using prior structural knowledge. To improve execution efficiency, an optimizer agent rewrites and merges multiple DAGs, eliminating redundant operations and minimizing computational and communicational overhead. Our experiments demonstrate that LAFA consistently outperforms baseline prompting strategies by achieving higher execution plan success rates and reducing resource-intensive FA operations by a substantial margin. This work establishes a practical foundation for privacy-preserving, LLM-driven analytics that supports natural language input in the FA setting.

cs.AI↗

arXiv2025

Joint Computing Resource Allocation and Task Offloading in Vehicular Fog Computing Systems Under Asymmetric Information

Vehicular fog computing (VFC) has emerged as a promising paradigm, which leverages the idle computational resources of nearby fog vehicles (FVs) to complement the computing capabilities of conventional vehicular edge computing. However, utilizing VFC to meet the delay-sensitive and computation-intensive requirements of the FVs poses several challenges. First, the limited resources of road side units (RSUs) struggle to accommodate the growing and diverse demands of vehicles. This limitation is further exacerbated by the information asymmetry between the controller and FVs due to the reluctance of FVs to disclose private information and to share resources voluntarily. This information asymmetry hinders the efficient resource allocation and coordination. Second, the heterogeneity in task requirements and the varying capabilities of RSUs and FVs complicate efficient task offloading, thereby resulting in inefficient resource utilization and potential performance degradation. To address these challenges, we first present a hierarchical VFC architecture that incorporates the computing capabilities of both RSUs and FVs. Then, we formulate a delay minimization optimization problem (DMOP), which is an NP-hard mixed integer nonlinear programming problem. To solve the DMOP, we propose a joint computing resource allocation and task offloading approach (JCRATOA). Specifically, we propose a convex optimization-based method for RSU resource allocation and a contract theory-based incentive mechanism for FV resource allocation. Moreover, we present a two-sided matching method for task offloading by employing the matching game. Simulation results demonstrate that the proposed JCRATOA is able to achieve superior performances in task completion delay, task completion ratio, system throughput, and resource utilization fairness, while effectively meeting the satisfying constraints.

cs.NI↗

arXiv2025

Cost Minimization for Space-Air-Ground Integrated Multi-Access Edge Computing Systems

Space-air-ground integrated multi-access edge computing (SAGIN-MEC) provides a promising solution for the rapidly developing low-altitude economy (LAE) to deliver flexible and wide-area computing services. However, fully realizing the potential of SAGIN-MEC in the LAE presents significant challenges, including coordinating decisions across heterogeneous nodes with different roles, modeling complex factors such as mobility and network variability, and handling real-time decision-making under partially observable environment with hybrid variables. To address these challenges, we first present a hierarchical SAGIN-MEC architecture that enables the coordination between user devices (UDs), uncrewed aerial vehicles (UAVs), and satellites. Then, we formulate a UD cost minimization optimization problem (UCMOP) to minimize the UD cost by jointly optimizing the task offloading ratio, UAV trajectory planning, computing resource allocation, and UD association. We show that the UCMOP is an NP-hard problem. To overcome this challenge, we propose a multi-agent deep deterministic policy gradient (MADDPG)-convex optimization and coalitional game (MADDPG-COCG) algorithm. Specifically, we employ the MADDPG algorithm to optimize the continuous temporal decisions for heterogeneous nodes in the partially observable SAGIN-MEC system. Moreover, we propose a convex optimization and coalitional game (COCG) method to enhance the conventional MADDPG by deterministically handling the hybrid and varying-dimensional decisions. Simulation results demonstrate that the proposed MADDPG-COCG algorithm significantly enhances the user-centric performances in terms of the aggregated UD cost, task completion delay, and UD energy consumption, with a slight increase in UAV energy consumption, compared to the benchmark algorithms. Moreover, the MADDPG-COCG algorithm shows superior convergence stability and scalability.

cs.LG↗

arXiv2025

Joint Optimization of Cooperation Efficiency and Communication Covertness for Target Detection with AUVs

This paper investigates underwater cooperative target detection using autonomous underwater vehicles (AUVs), with a focus on the critical trade-off between cooperation efficiency and communication covertness. To tackle this challenge, we first formulate a joint trajectory and power control optimization problem, and then present an innovative hierarchical action management framework to solve it. According to the hierarchical formulation, at the macro level, the master AUV models the agent selection process as a Markov decision process and deploys the proximal policy optimization algorithm for strategic task allocation. At the micro level, each selected agent's decentralized decision-making is modeled as a partially observable Markov decision process, and a multi-agent proximal policy optimization algorithm is used to dynamically adjust its trajectory and transmission power based on its local observations. Under the centralized training and decentralized execution paradigm, our target detection framework enables adaptive covert cooperation while satisfying both energy and mobility constraints. By comprehensively modeling the considered system, the involved signals and tasks, as well as energy consumption, theoretical insights and practical solutions for the efficient and secure operation of multiple AUVs are provided, offering significant implications for the execution of underwater covert communication tasks.

cs.LG↗

arXiv2025

Channel Capacity for FMCW-based Optical Wireless Integrated Sensing and Communication: Asymptotic Analysis and Envelope Design

Optical wireless integrated sensing and communication (OW-ISAC) is rapidly burgeoning as a complement and augmentation to its radio-frequency counterpart. In this paper, the channel capacity is analyzed to guide the design of a coherent OW-ISAC system based on frequency-modulated continuous wave (FMCW). Firstly, the system model of FMCW-based OW-ISAC is recast into an information-theoretic formulation, where an additional harmonic-mean constraint is imposed to ensure the sensing performance. Subsequently, both lower and upper bounds for channel capacity are derived under the imposed sensing constraint, based on which asymptotic expressions for channel capacity are presented for both low and high signal-to-noise-ratio regions. Moreover, the analysis of channel capacity provides guidance for the envelope design based on pulse amplitude modulation, whose capacity-achieving capabilities are demonstrated by numerical results. Furthermore, simulations reveal the trade-off between communication and sensing functionalities. In summary, the analysis of channel capacity under the sensing constraint provides insights into both the optimality and the practicality of OW-ISAC design.

cs.IT↗

arXiv2025

Semantic Communication Enabled Holographic Video Processing and Transmission

Holographic video communication is considered a paradigm shift in visual communications, becoming increasingly popular for its ability to offer immersive experiences. This article provides an overview of holographic video communication and outlines the requirements of a holographic video communication system. Particularly, following a brief review of semantic com- munication, an architecture for a semantic-enabled holographic video communication system is presented. Key technologies, including semantic sampling, joint semantic-channel coding, and semantic-aware transmission, are designed based on the proposed architecture. Two related use cases are presented to demonstrate the performance gain of the proposed methods. Finally, potential research topics are discussed to pave the way for the realization of semantic-enabled holographic video communications.

eess.IV↗

arXiv2025

Multilayer Non-Terrestrial Networks with Spectrum Access aided by Beyond-Diagonal RIS

In this work, we study a multi-user NTN in which a satellite serves as the primary network and a high-altitude platform station (HAPS) operates as the secondary network, acting as a cognitive radio. To reduce the cost, complexity, and power consumption of conventional antenna arrays, we equip the HAPS with a transmissive BD-RIS antenna front end. We then formulate a joint optimization problem for the BD-RIS phase response and the HAPS transmit power allocation under strict per-user interference temperature constraints. To tackle the resulting highly nonconvex problem, we propose an alternating-optimization framework: the power-allocation subproblem admits a closed-form, water-filling-type solution derived from the Karush-Kuhn-Tucker (KKT) conditions, while the BD-RIS configuration is refined via Riemannian manifold optimization. Simulation results show significant gains in data rate and interference suppression over diagonal RIS-assisted benchmarks, establishing BD-RIS as a promising enabler for future multilayer NTNs.

eess.SP↗

arXiv2025

A Conformal Prediction-Based Chance-Constrained Programming Approach for 24/7 Carbon-Free Data Center Operation Scheduling

The rapid growth of AI applications is dramatically increasing data center energy demand, exacerbating carbon emissions, and necessitating a shift towards 24/7 carbon-free energy (CFE). Unlike traditional annual energy matching, 24/7 CFE requires matching real-time electricity consumption with clean energy generation every hour, presenting significant challenges due to the inherent variability and forecasting errors of renewable energy sources. Traditional robust and data-driven optimization methods often fail to leverage the features of the prediction model (also known as contextual or covariate information) when constructing the uncertainty set, leading to overly conservative operational decisions. This paper proposes a comprehensive approach for 24/7 CFE data center operation scheduling, focusing on robust decision-making under renewable generation uncertainty. This framework leverages covariate information through a multi-variable conformal prediction (CP) technique to construct statistically valid and adaptive uncertainty sets for renewable forecasts. The uncertainty sets directly inform the chance-constrained programming (CCP) problem, ensuring that chance constraints are met with a specified probability. We further establish theoretical underpinnings connecting the CP-generated uncertainty sets to the statistical feasibility guarantees of the CCP. Numerical results highlight the benefits of this covariate-aware approach, demonstrating up to 6.65% cost reduction and 6.96% decrease in carbon-based energy usage compared to conventional covariate-independent methods, thereby enabling data centers to progress toward 24/7 CEF.

eess.SY↗

arXiv2025

A Contemporary Survey on Semantic Communications:Theory of Mind, Generative AI, and Deep Joint Source-Channel Coding

Semantic communication is emerging as the next pillar in wireless communication technology due to its transformative capabilities in reducing communication overhead, enhancing robustness, and enabling intelligent information exchange. The most significant obstacle lies in the lack of standardization across various research directions, leading to inconsistencies in interpretation, objectives, and evaluation. In this survey, we provide an in-depth overview of three leading directions in semantic communication, namely Theory of Mind-based semantic communication, Generative AI-driven semantic communication, and Deep Joint Source-Channel Coding (DJSCC)-based semantic communication. These directions have been extensively studied and developed by research institutes worldwide, and their effectiveness continues to improve alongside advances in communication and computing technologies. The ToM-based semantic communication enables communication agents to interact intelligently, infer each other's intentions, and gradually form a shared understanding. The GAI-based semantic communication leverages generative models to create and interpret content beyond traditional compression, allowing flexible semantic encoding and decoding tailored to specific tasks. The DJSCC-based semantic communication direction integrates DL models to jointly optimize the source and channel coding processes for efficient semantic information transfer. Next, we present a detailed survey of existing works under each direction and open research problems in semantic communication. Furthermore, we identify and analyze critical challenges, such as scalability and adaptability, that currently hinder the deployment of semantic communication systems. Finally, we discuss potential research opportunities and future directions such as quantum computing to further enhance the capabilities of semantic communication.

cs.IT↗

arXiv2025

A Deep Incremental Framework for Multi-Service Multi-Modal Devices in NextG AI-RAN Systems

In this paper, we propose a deep incremental framework for efficient RAN management, introducing the Multi-Service-Modal UE (MSMU) system, which enables a single UE to handle eMBB and uRLLC services simultaneously. We formulate an optimization problem integrating traffic demand prediction, route optimization, RAN slicing, service identification, and radio resource management under uncertainty. We decompose it into long-term (L-SP) and short-term (S-SP) subproblems then propose a Transformer model for L-SP optimization, predicting eMBB and uRLLC traffic demands and optimizing routes for RAN slicing. To address non-stationary network traffic with evolving trends and scale variations, we integrate reversible instance normalization (ReVIN) into the forecasting pipeline. For the S-SP, we propose an LSTM model enabling real-time service type identification and resource management, utilizing L-SP predictions. We incorporate continual learning into the S-SP framework to adapt to new service types while preserving prior knowledge. Experimental results demonstrate that our proposed framework achieves up to 46.86% reduction in traffic demand prediction error, 26.70% and 18.79% improvement in PRBs and power estimation, 7.23% higher route selection accuracy, and 7.29% improvement in service identification over the baselines with 95% average accuracy in continual service identification across seven sequential tasks.

cs.NI↗

arXiv2025

Wideband Integrated Sensing and Communications: Spectral Efficiency and Signaling Design

In integrated sensing and communications (ISAC), a distinguishing feature of 6G wireless networks, the main challenge lies in integrating the two distinct functions of sensing and communication within the same waveform. In this paper, the ISAC waveform synthesis is studied in the wideband regime, since a large bandwidth can simplify the analysis and is justified by the employment of millimeter wave or higher frequency band. Standard orthogonal frequency division multiplexing (OFDM) signaling is assumed, and the wideband analysis of sensing is a counterpart of the existing studies on wideband communications. It is proposed that the phase over such OFDM subcarriers is for modulating communication messages while the power spectral density (PSD) is shaped for the sensing performance. Beyond OFDM, we further reveal a duality between the proposed PSD-shaping rule and the orthogonal time frequency space (OTFS) waveform. Flattening the OTFS delay-axis PSD produces the same integrated sidelobe level (ISL) reduction effect in the delay-Doppler domain as PSD control achieves for OFDM in the frequency domain. To balance communication and sensing performance over frequency-selective channels, we propose a low-complexity, water-filling-like allocator with an explicit PSD-flatness (variance) constraint. The performance of the proposed wideband ISAC scheme is demonstrated using both numerical simulations and hardware experiments.

eess.SP↗