arXiv subjects

Qinyu Zhang

Publications and source records attributed to Qinyu Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Neuro-Symbolic Agentic AI for Networked Low-Altitude UAVs

Networked low-altitude unmanned aerial vehicles (UAVs) need reliable and adaptive decision-making capabilities to operate under uncertain observations, dynamic environments, and intermittent connectivity, while many existing agentic systems remain limited by hallucination risks, data dependence, and weak generalization. This article investigates neuro-symbolic agentic AI (NSAAI) as a framework for combining neural grounding, symbolic reasoning, and closed-loop agentic interaction to support more reliable and adaptive UAV autonomy. We first examine its capability foundations in data efficiency, compositional generalization, continual learning, and zero-shot transfer, and then develop a reference architecture integrating task and goal management, neuro-symbolic planning, verification and metacognition, skill execution and network interaction, and shared knowledge and memory. An urban fire-inspection case implemented in LAESim illustrates how a UAV can coordinate sensing and cloud access under intermittent connectivity, reuse a verified image-delivery skill, and satisfy explicit evidence conditions before completing the mission. The results illustrate the potential of NSAAI to support reusable skills, evidence-grounded decision-making, and adaptive mission execution in networked UAV systems. We further discuss key research directions in uncertainty-aware reasoning, knowledge and skill expansion, adaptive self-monitoring, and standardized evaluation.

cs.AI↗

arXiv2026

Overlap-Summation-Based Pulse Shaping Transceiver for Affine Frequency Division Multiplexing

Affine frequency division multiplexing (AFDM) has recently emerged as a promising waveform for doubly-selective channels. A direct-windowing-based pulse shaping transceiver (PS-AFDM) was proposed to suppress the Doppler sidelobes, thus improving the accuracy of channel estimation. We observe that the legacy PS-AFDM significantly increases the condition number of the effective channel matrix when path delay and Doppler parameters are randomly distributed, such ill-conditioning leads to the degradation in the solution stability of channel equalization under noisy conditions, thus resulting in the degradation of bit error rate (BER). To address this issue, this letter applies the existing weighted overlap-summation (WOLA) transceiver to AFDM and proposes a novel channel-aware (CA) receive shaping window design, which simultaneously achieves accurate channel estimation and robust equalization performance, at the cost of time-domain prefix and receive window calculation overheads. The resulting scheme is termed CAWOLA-AFDM. Compared with the legacy WOLA scheme, which employs a fixed receive window, the proposed CAWOLA design exploits the non-instantaneous channel estimates of power gains and Doppler shifts to design a channel-tailored and closed-form receive shaping window, thereby further enhancing channel estimation accuracy while maintaining the channel condition number when a Nyquist prototype window is adopted. The proposed CAWOLA receive window design aims to provide the effective AFDM channel with a more compact DAFT-domain support. The source codes for the simulations are provided at https://github.com/SANIS-HITSZ/Waveform_AFDM.

eess.SP↗

arXiv2026

Twinning for Space-Air-Ground-Sea Integrated Networks: Beyond Conventional Digital Twin Towards Goal-Oriented Semantic Twin

A space-air-ground-sea integrated network (SAGSIN) has emerged as a cornerstone of 6G systems, establishing a unified global architecture by integrating multi-domain network resources. Motivated by the demand for real-time situational awareness and intelligent operational maintenance, digital twin (DT) technology was initially regarded as a promising solution, owing to its capability to create virtual replicas and emulate physical system behaviors. However, in the context of SAGSIN, the high-fidelity, full-scale modeling paradigm inherent to conventional DTs encounters fundamental limitations, including prohibitive computational overhead, delayed model synchronization, and cross-system semantic gaps. To address these limitations, this survey paper proposes a novel twinning framework: goal-oriented semantic twin (GOST). Unlike DTs that pursue physical mirroring, GOST prioritizes ``utility'' over ``fidelity,'' leveraging semantic technologies and goal-oriented principles to construct lightweight, task-specific representations. This paper systematically articulates the GOST framework through three layers: knowledge-based semantics, data-driven semantics, and goal-oriented principles. Furthermore, we provide a comprehensive tutorial on constructing GOST by detailing its core enabling technologies and introduce a multidimensional evaluation framework for GOST. We present a case study targeting collaborative tracking tasks in remote satellite-UAV networks, demonstrating that GOST significantly outperforms conventional DTs in timeliness of perceptual data and collaborative tracking. Finally, we outline research directions, establishing GOST as a transformative twinning paradigm to guide the development of SAGSIN.

cs.DC↗

arXiv2026

Distributed Integrated Sensing and Edge AI Exploiting Prior Information

This paper investigates a distributed ISEA system under a Bayesian framework, focusing on incorporating task-relevant priors to maximize inference performance. At the sensing level, an RWB estimator with a GM prior is designed. By weighting class-conditional posterior means with responsibilities, RWB effectively denoises features and outperforms ML at low SNR. At the communication level, two theoretical proxies are introduced: the computation-optimal and decision-optimal proxies. Optimal transceiver designs in terms of closed-form power allocation are derived for both TDM and FDM settings, revealing threshold-based and dual-decomposition structures. Results show that the discriminant-aware allocation yields additional inference gains.

eess.SP↗

arXiv2026

GEAR: Reconstruction of Classical Paintings via Geometry Grounding and Appearance Restitution

Classical paintings preserve rich spatial, cultural, and historical content, making their reconstruction as explorable 3D scenes valuable for digital preservation, immersive exhibition, and cultural engagement. Yet, unlike photographs, they often depict scenes in a single-view, stylized manner, with weak perspective, lighting, and depth cues. Existing 3D reconstruction methods are largely built on natural-image priors, making it difficult to recover geometrically plausible and visually faithful 3D representations from such inputs. To address this challenge, we introduce Classical Painting-to-3D (CP3D), a new task that aims to recover a 3D representation from a single classical painting while jointly ensuring geometric plausibility, appearance fidelity to the source artwork, and plausible novel-view synthesis. We further propose GeAR, a training-free two-stage framework for Geometry Grounding and Appearance Restitution. GeAR first converts the input painting into a geometry-grounded representation with more coherent shading and illumination cues, improving the stability of 3D Gaussian reconstruction. It then restores artwork-faithful appearance across views under spatial constraints and multi-view consistency, recovering the painterly textures and details weakened during grounding. In addition, we construct HeriArch, a curated benchmark of 10,160 high-resolution classical artworks for systematic evaluation of CP3D. Extensive experiments and user studies show that GeAR consistently outperforms strong baselines in geometric plausibility, appearance fidelity, and human preference. Code and dataset will be released publicly.

cs.MM↗

arXiv2026

Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition

Fine-grained image recognition poses a significant challenge due to the substantial expertise and effort required for manual annotation. Vision-language models (VLMs) like CLIP provide a compelling zero-shot alternative, reducing reliance on extensive labeled data. However, their ability to capture subtle distinctions remains limited, leading to subpar recognition performance. While prompt tuning has proven effective for adapting VLMs, most existing methods treat class labels as isolated, discrete entities, overlooking the rich semantic relationships between them. This oversimplified assumption limits the model's ability to capture hierarchical dependencies and inter-class correlations -- both critical for distinguishing visually similar categories. The problem is especially acute in fine-grained classification, where accurate recognition depends on understanding complex label semantics. To address this, we propose Structured-Condensed Prompt Tuning (SCPT), which enhances semantic structure modeling in prompt learning. Specifically, we introduce Semantic Relation Encoding (SRE) to explicitly model inter-class semantic topology and encode structured label relationships. In parallel, we design a Semantic Condensation loss (ScLoss) to suppress redundant supervision and extract discriminative components from the global semantic space. Together, these components significantly improve semantic alignment and fine-grained discrimination. Extensive experiments on 14 fine-grained benchmarks show that SCPT effectively mitigates semantic ambiguity and achieves state-of-the-art performance in both few-shot and base-to-novel generalization settings.

cs.CV↗

arXiv2026

Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmission with Imperfect Channel State Information

This work is concerned with robust distributed multi-view image transmission over a severe fading channel with imperfect channel state information (CSI), wherein the sources are slightly correlated. Since the signals are further distorted at the decoder, traditional distributed deep joint source-channel coding (DJSCC) suffers considerable performance degradation. To tackle this problem, we leverage the complementarity and consistency characteristics among the distributed, yet correlated sources, and propose an enhanced robust DJSCC, namely RDJSCC. In RDJSCC, we design a novel cross-view information extraction (CVIE) mechanism to capture more nuanced cross-view patterns and dependencies. In addition, a complementarity-consistency fusion (CCF) mechanism is utilized to fuse the complementarity and consistency from multi-view information in a symmetric and compact manner. Theoretical analysis and simulation results show that our proposed RDJSCC can effectively leverage the advantages of correlated sources even under severe fading conditions, leading to an improved reconstruction performance. The open source code of this work is available at:https://dongbiao26.github.io/rdjscc/.

eess.SP↗

arXiv2026

UAV Control and Communication Enabled Low-Altitude Economy: Challenges, Resilient Architecture and Co-design Strategies

The emerging low-altitude economy has catalyzed the large-scale deployment of unmanned aerial vehicles (UAVs), driving a paradigm shift in environment monitoring, logistics, and emergency response. However, operating within these environments presents notable challenges as pervasive coverage holes, unpredictable interference, and spectrum scarcity. To this end, this article present a communication and control co-design framework to enable a resilient architecture for cellular-connected UAVs. Specifically, we first characterize typical service applications and their stringent performance requirements, followed by a comprehensive analysis of the unique challenges. To bridge the gap between volatile wireless links and rigid flight stability, a three layered architecture is proposed, integrating pre-flight strategic planning, in-flight adaptive action, and system-level resource orchestration. Furthermore, we detail the key enabling technologies for communication and control co-design. Preliminary case studies are proposed to validate that the co-design framework significantly improve the resilience of cellular-connected UAV systems, providing a robust foundation for the evolution of intelligent low-altitude networks.

cs.NI↗

arXiv2026

Towards Semantic-based Agent Communication Networks: Vision, Technologies, and Challenges

The International Telecommunication Union (ITU) identifies "Artificial Intelligence (AI) and Communication" as one of six key usage scenarios for 6G. Agentic AI, characterized by its ca-pabilities in multi-modal environmental sensing, complex task coordination, and continuous self-optimization, is anticipated to drive the evolution toward agent-based communication net-works. Semantic communication (SemCom), in turn, has emerged as a transformative paradigm that offers task-oriented efficiency, enhanced reliability in complex environments, and dynamic adaptation in resource allocation. However, comprehensive reviews that trace their technologi-cal evolution in the contexts of agent communications remain scarce. Addressing this gap, this paper systematically explores the role of semantics in agent communication networks. We first propose a novel architecture for semantic-based agent communication networks, structured into three layers, four entities, and four stages. Three wireless agent network layers define the logical structure and organization of entity interactions: the intention extraction and understanding layer, the semantic encoding and processing layer, and the distributed autonomy and collabora-tion layer. Across these layers, four AI agent entities, namely embodied agents, communication agents, network agents, and application agents, coexist and perform distinct tasks. Furthermore, four operational stages of semantic-enhanced agentic AI systems, namely perception, memory, reasoning, and action, form a cognitive cycle guiding agent behavior. Based on the proposed architecture, we provide a comprehensive review of the state-of-the-art on how semantics en-hance agent communication networks. Finally, we identify key challenges and present potential solutions to offer directional guidance for future research in this emerging field.

eess.SP↗

arXiv2025

Inference-Optimal ISAC via Task-Oriented Feature Transmission and Power Allocation

This work is concerned with the coordination gain in integrated sensing and communication (ISAC) systems under a compress-and-estimate (CE) framework, wherein inference performance is leveraged as the key metric. To enable tractable transceiver design and resource optimization, we characterize inference performance via an error probability bound as a monotonic function of the discriminant gain (DG). This raises the natural question of whether maximizing DG, rather than minimizing mean squared error (MSE), can yield better inference performance. Closed-form solutions for DG-optimal and MSE-optimal transceiver designs are derived, revealing water-filling-type structures and explicit sensing and communication (S\&C) tradeoff. Numerical experiments confirm that DG-optimal design achieves more power-efficient transmission, especially in the low signal-to-noise ratio (SNR) regime, by selectively allocating power to informative features and thus saving transmit power for sensing.

eess.SP↗

arXiv2025

Towards Goal-Oriented Semantic Communications: New Metrics, Framework, and Open Challenges

Since Shannon's pioneering masterpiece which established the prototype of modern information theory, the goal of communications has long been promising accurate message reconstruction at a refined bit-by-bit level, which deliberately neglects the semantics/effectiveness aspect of information. Nevertheless, the recent development of wireless technologies and the spurt of deep learning (DL) techniques allow us to reclaim the meaning/usefulness aspect in the design of future 6G communication paradigms, where goal-oriented communication is becoming a trend. Age of Information (AoI), a well-known metric that captures the importance of information by recording the time elapsed from the generation time slot, has been extended to various variants, such as Value of Information (VoI), Urgency of Information (UoI), Age of Incorrect Information (AoII), and etc. While each of them proposes novel ways to measure the meaning/usefulness aspect of information, there is not yet an integrated framework encompassing all of them. To this end, we propose a novel tensor-based approach, the Goal-oriented Tensor (GoT), to unify them, which also allows more flexible and fine-grained goal characterizations. Following the proposed GoT, we architect a holistic goal-oriented framework to enable goal-oriented semantic communications, in which information perception, dissemination, and control-plane decisions are implemented in accordance with specific goals. Finally, we outline several open challenges to fulfill the vision of the GoT framework.

cs.IT↗

arXiv2025

Task-Agnostic Learnable Weighted-Knowledge Base Scheme for Robust Semantic Communications

With the emergence of diverse and massive data in the upcoming sixth-generation (6G) networks, the task-agnostic semantic communication system is regarded to provide robust intelligent services. In this paper, we propose a task-agnostic learnable weighted-knowledge base semantic communication (TALSC) framework for robust image transmission to address the real-world heterogeneous data bias in KB, including label flipping noise and class imbalance. The TALSC framework incorporates a sample confidence module (SCM) as meta-learner and the semantic coding networks as learners. The learners are updated based on the empirical knowledge provided by the learnable weighted-KB (LW-KB). Meanwhile, the meta-learner evaluates the significance of samples according to the task loss feedback, and adjusts the update strategy of learners to enhance the robustness in semantic recovery for unknown tasks. To strike a balance between SCM parameters and precision of significance evaluation, we design an SCM-grid extension (SCM-GE) approach by embedding the Kolmogorov-Arnold networks (KAN) within SCM, which leverages the concept of spline refinement in KAN and enables scalable SCM with customizable granularity without retraining. Simulations demonstrate that the TALSC framework effectively mitigates the effects of flipping noise and class imbalance in task-agnostic image semantic communication, achieving at least 12% higher semantic recovery accuracy (SRA) and multi-scale structural similarity (MS-SSIM) compared to state-of-the-art methods.

cs.IT↗

arXiv2025

Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition

The rapid development of the low-altitude economy emphasizes the critical need for effective perception and intent recognition of non-cooperative unmanned aerial vehicles (UAVs). The advanced generative reasoning capabilities of multimodal large language models (MLLMs) present a promising approach in such tasks. In this paper, we focus on the combination of UAV intent recognition and the MLLMs. Specifically, we first present an MLLM-enabled UAV intent recognition architecture, where the multimodal perception system is utilized to obtain real-time payload and motion information of UAVs, generating structured input information, and MLLM outputs intent recognition results by incorporating environmental information, prior knowledge, and tactical preferences. Subsequently, we review the related work and demonstrate their progress within the proposed architecture. Then, a use case for low-altitude confrontation is conducted to demonstrate the feasibility of our architecture and offer valuable insights for practical system design. Finally, the future challenges are discussed, followed by corresponding strategic recommendations for further applications.

eess.SY↗

arXiv2025

Scenario-Agnostic Deep-Learning-Based Localization with Contrastive Self-Supervised Pre-training

Wireless localization has become a promising technology for offering intelligent location-based services. Although its localization accuracy is improved under specific scenarios, the short of environmental dynamic vulnerability still hinders this approach from being fully practical applications. In this paper, we propose CSSLoc, a novel framework on contrastive self-supervised pre-training to learn generic representations for accurate localization in various scenarios. Without the location information supervision, CSSLoc attempts to learn an insightful metric on the similarity discrimination of radio data, in such a scenario-agnostic manner that the similar samples are closely clustered together and different samples are separated in the representation space. Furthermore, the trained feature encoder can be directly transferred for downstream localization tasks, and the location predictor is trained to estimate accurate locations with the robustness of environmental dynamics. With extensive experimental results, CSSLoc can outperform classical and state-of-the-art DNN-based localization schemes in typical indoor scenarios, pushing deep-learning-based localization from specificity to generality.

eess.SP↗

arXiv2025

Integrated Sensing, Communication, and Computation Over-the-Air in OFDM Systems

This work is concerned with integrated sensing, communication, and computation (ISCC) in uplink orthogonal frequency division multiplexing (OFDM) systems, wherein multiple devices perform target sensing and over-the-air computation (AirComp) simultaneously. We aim to minimize the computational mean squared error (MSE) by jointly optimizing the transmitting vector and the aggregation vector. To tackle the non-convexity of this problem, we develop a two-phase iterative algorithm. Simulations demonstrate the effectiveness of the proposed algorithm.

eess.SP↗

arXiv2025

Distributed satellite information networks: Architecture, enabling technologies, and trends

Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, grant-free massive access, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

cs.IT↗

arXiv2025

Fundamental Limits of Pulse Based UWB ISAC Systems: A Parameter Estimation Perspective

This paper investigates a bi-static integrated sensing and communication (ISAC) system for multi-target scenarios using impulse radio ultra-wideband (IR-UWB) signals, which offer fine temporal resolution, low power consumption, and strong resistance to multipath interference. Two typical modulation schemes, namely pulse position modulation (PPM) and binary phase shift keying (BPSK), are considered for communication over the delay and phase domains, respectively. Accordingly, we introduce a pilot-based decoupling approach that relies on known time-delays, as well as a differential decoupling strategy that uses a known starting symbol position ({no pilot required}). A key contribution of this work is the development of a unified analytical framework based on the Fisher Information Matrix (FIM), which characterizes the fundamental coupling between communication and sensing in both delay and Doppler domains. This coupling is examined through the singularity structure of the FIM, providing new theoretical insights into the joint performance limits of UWB-ISAC systems. Finally, we assess the sensing and communication performance under various modulation schemes under the constraints of current UWB standards. This assessment utilizes the Cramer-Rao Lower Bound (CRLB) for sensing and the data transmission rate for communication, offering theoretical insights into choosing suitable data signal processing methods in real-world applications.

eess.SP↗

arXiv2025

Integrating Sensing and Communications in 6G? Not Until It Is Secure to Do So

Integrated Sensing and Communication (ISAC) is emerging as a cornerstone technology for forthcoming 6G systems, significantly improving spectrum and energy efficiency. However, the commercial viability of ISAC hinges on addressing critical challenges surrounding security, privacy, and trustworthiness. These challenges necessitate an end-to-end framework to safeguards both communication data and sensing information, particularly in ultra-low-latency and highly connected environments. Conventional solutions, such as encryption and key management, often fall short when confronted with ISAC's dual-functional nature. In this context, the physical layer plays a pivotal role: this article reviews emerging physical-layer strategies, including artificial noise (AN) injection, cooperative jamming, and constructive interference (CI), which enhance security by mitigating eavesdropping risks and safeguarding both communication data and sensing information. We further highlight the unique privacy issues that ISAC introduces to cellular networks and outline future research directions aimed at ensuring robust security and privacy for efficient ISAC deployment in 6G.

cs.IT↗