arXiv subjects

Yuanhao Cui

Publications and source records attributed to Yuanhao Cui.

At least 19 recordsLinked to original sources

arXiv2026

Relativistic Cram\'er-Rao Bound Scaling for Device-Based and Device-Free Sensing

This letter investigates range and velocity estimation under relativistic motion for device-based (DB) and device-free (DF) sensing. By deriving the exact time-scaling and time-shift relations induced by one-way and two-way propagation, both sensing modes are cast into a unified affine signal model. Closed-form Cram\'er--Rao bounds (CRBs) are obtained as explicit functions of normalized velocity, root-mean-squared (RMS) bandwidth, and RMS duration. The bounds recover the classical low-speed results but exhibit distinct velocity scaling in the ultrarelativistic regime. For rapidly receding motion, the range CRB diverges while the velocity CRB vanishes. For rapidly approaching motion, both CRBs vanish. The DB and DF modes further exhibit different asymptotic orders in the two directions, showing that relativistic motion changes not only the signal model but also the fundamental scaling laws governing sensing accuracy.

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arXiv2026

RFCheck: Synthetic RF Sensing Data Can Fail Measurement Consistency

Synthetic radio-frequency (RF) sensing data are widely used to augment wireless sensing tasks, yet their measurement consistency with real data is rarely evaluated under matched acquisition conditions. This paper identifies a measurement-consistency failure mode: synthetic samples may pass task-facing checks while deviating from the measurement behavior of real samples collected and processed by the same sensing pipeline, potentially introducing synthetic shortcuts and biasing downstream model selection. We propose RFCheck, a calibrated measurement audit that uses held-out real data from the same acquisition and preprocessing pipeline as the reference. RFCheck calibrates representation-specific tests on real samples and flags synthetic samples whose responses exceed the calibrated real-data range. We use the audit for candidate screening and residual repair. We validate RFCheck primarily on Wi-Fi channel state information (CSI), where the audit examines delay-domain and local frequency-domain structures. Experiments show that aggregate statistics and label-based screening can miss measurement failures detected by RFCheck. Under the same label acceptance rule, low-risk and high-risk synthetic candidates exhibit different downstream behavior. A repair reference reduces the flagged ratio to 10.83% while preserving mean task performance. In a held-out proposal study, correction followed by calibrated selection produces a class-balanced set with no flagged samples under a fixed training budget. We further apply the same calibration principle to frequency-modulated continuous-wave (FMCW) millimeter-wave radar gesture sensing. The results show that synthetic RF sensing data can violate measurement consistency even when conventional task checks are satisfied, motivating measurement-aware diagnosis and mitigation before augmentation.

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arXiv2026

Toward Intelligent Skies: Signal Processing and AI Foundations of Low-Altitude Wireless Networks

The rapid growth of low-altitude aerial services and applications, driven by uncrewed aerial vehicles (UAVs), calls for a new class of digital infrastructure beyond conventional terrestrial networks. The low-altitude wireless network (LAWN) has been proposed as dynamically reconfigurable three-dimensional architectures that integrate aerial and ground nodes to provide connectivity, sensing, and control in open, safety-critical airspace. This tutorial presents a comprehensive treatment of LAWNs from the joint perspectives of artificial intelligence (AI) and signal processing. We first review the historical evolution and architectural foundations of LAWNs, introducing altitude-based layers and functional planes, and summarizing the regulatory and standardization landscape. Building on this system view, we then discuss signal processing fundamentals for LAWNs, including 3D channel and system models, performance metrics, waveform and receiver design, localization and tracking, and multi-functionality co-design. Next, we survey AI techniques for LAWNs, covering discriminative and generative models for perception, control, resource management, and security, as well as emerging paradigms such as foundation models, large language models, and digital twins for mission planning and closed-loop optimization. To illustrate AI-signal processing integration in practice, we provide a case study of an AI-driven multi-tier LAWN with hybrid satellite, high-altitude, and ground nodes. The tutorial concludes by outlining key research challenges in architecture design, signal processing-AI co-design, safety and security, experimentation, and standardization, and by highlighting opportunities for LAWNs to evolve into dependable, AI-native infrastructure for the intelligent skies.

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arXiv2026

Radar-Aided Near-Field Beam Prediction via Beam Map Learning for XL-MIMO V2I Communications

Near-field beam training in extremely large-scale multiple-input multiple-output (XL-MIMO) vehicle-to-infrastructure (V2I) systems incurs high overhead due to large range-angle codebooks and rapid channel variation. This paper proposes a passive radar-aided framework for near-field beam prediction based on radar-to-beam map learning. By exploiting the spatial correlation between radar observations and communication signals, the proposed method maps radar Bartlett spectra to communication beam maps using a lightweight encoder-decoder convolutional neural network. Gaussian soft supervision is further introduced to preserve beam-space continuity. Simulations on a synchronized Sionna ray tracing radar-communication dataset show that the proposed method consistently improves Top-k accuracy, distance-based accuracy, beam loss, and spectral efficiency.

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arXiv2026

Low-Altitude Wireless Networks: The Next Horizon of Wireless Infrastructure

Low-altitude airspace, roughly defined as the region up to 3000 meters above ground level, is envisioned as a new spatial domain for daily human and machine activities. This article introduces the concept of the Low-Altitude Wireless Network (LAWN), which represents a paradigm shift from the current ground-based communication-only network to a three-dimensional (3D) multifunctional network. We analyze the key driving forces, network architecture, and limiting factors of LAWN, with a particular focus on the tight integration of communication, sensing, and control in highly dynamic airspace environments. By establishing the coupling between airspace capacity and wireless channel capacity, we reveal the intrinsic limits of airspace management and identify the fundamental challenges and opportunities associated with its evolution.

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arXiv2026

Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks

Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks.

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arXiv2026

SDP: A Unified Protocol and Benchmarking Framework for Reproducible Wireless Sensing

Learning-based wireless sensing has made rapid progress, yet the field still lacks a unified and reproducible experimental foundation. Unlike computer vision, wireless sensing relies on hardware-dependent channel measurements whose representations, preprocessing pipelines, and evaluation protocols vary significantly across devices and datasets, hindering fair comparison and reproducibility. This paper proposes the Sensing Data Protocol (SDP), a protocol-level abstraction and unified benchmark for scalable wireless sensing. SDP acts as a standardization layer that decouples learning tasks from hardware heterogeneity. To this end, SDP enforces deterministic physical-layer sanitization, canonical tensor construction, and standardized training and evaluation procedures, decoupling learning performance from hardware-specific artifacts. Rather than introducing task-specific models, SDP establishes a principled protocol foundation for fair evaluation across diverse sensing tasks and platforms. Extensive experiments demonstrate that SDP achieves competitive accuracy while substantially improving stability, reducing inter-seed performance variance by orders of magnitude on complex activity recognition tasks. A real-world experiment using commercial off-the-shelf Wi-Fi hardware further illustrating the protocol's interoperability across heterogeneous hardware. By providing a unified protocol and benchmark, SDP enables reproducible and comparable wireless sensing research and supports the transition from ad hoc experimentation toward reliable engineering practice.

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arXiv2025

Integrating Low-Altitude SAR Imaging into UAV Data Backhaul

Synthetic aperture radar (SAR) on unmanned aerial vehicles (UAVs) enables high-resolution sensing in low-altitude wireless networks, while requiring reliable uplink data backhaul to ground base stations under dynamic channel conditions. Conventional orthogonal frequency division multiplexing (OFDM)-based SAR systems rely on pilot or deterministic signaling, which occupies only a small fraction of the available timefrequency (TF) resources and limits imaging performance. This paper develops a data-aided OFDM-SAR imaging framework that reuses uplink communication data symbols for sensing, thereby exploiting the dominant TF resources of the UAV backhaul link. However, the randomness of data symbols disrupts the coherent structure required for SAR imaging, especially in highly dynamic channels with strong TF coupling, leading to severe degradation in range-Doppler focusing. To address this issue, we establish a unified TF domain filtering framework to suppress data-induced randomness and recover an equivalent deterministic imaging channel. Within this framework, reciprocal, matched, and Wiener filtering are interpreted under a common formulation, enabling a systematic characterization of their impact on imaging performance. A normalized mean square error (NMSE) metric of a reference point target's profile is further adopted to quantify the joint effects of randomnessinduced distortion and noise amplification. Simulation results based on 5G NR parameters show that the proposed dataaided scheme significantly outperforms pilot-only approaches by leveraging uplink data resources, demonstrating that effective TF-domain filtering is essential to ensure high-resolution imaging in dynamic UAV channels.

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arXiv2025

From Optimization to Learning: Dual-Approach Resource Allocation for Over-the-Air Edge Computing Under Execution Uncertainty

The exponential proliferation of mobile devices and data-intensive applications in future wireless networks imposes substantial computational burdens on resource-constrained devices, thereby fostering the emergence of over-the-air computation (AirComp) as a transformative paradigm for edge intelligence.} To enhance the efficiency and scalability of AirComp systems, this paper proposes a comprehensive dual-approach framework that systematically transitions from traditional mathematical optimization to deep reinforcement learning (DRL) for resource allocation under execution uncertainty. Specifically, we establish a rigorous system model capturing execution uncertainty via Gamma-distributed computational workloads, resulting in challenging nonlinear optimization problems involving complex Gamma functions. For single-user scenarios, we design advanced block coordinate descent (BCD) and majorization-maximization (MM) algorithms, which yield semi-closed-form solutions with provable performance guarantees. However, conventional optimization approaches become computationally intractable in dynamic multi-user environments due to inter-user interference and resource contention. To this end, we introduce a Deep Q-Network (DQN)-based DRL framework capable of adaptively learning optimal policies through environment interaction. Our dual methodology effectively bridges analytical tractability with adaptive intelligence, leveraging optimization for foundational insight and learning for real-time adaptability. Extensive numerical results corroborate the performance gains achieved via increased edge server density and validate the superiority of our optimization-to-learning paradigm in next-generation AirComp systems.

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arXiv2025

A Sensing Dataset Protocol for Benchmarking and Multi-Task Wireless Sensing

Wireless sensing has become a fundamental enabler for intelligent environments, supporting applications such as human detection, activity recognition, localization, and vital sign monitoring. Despite rapid advances, existing datasets and pipelines remain fragmented across sensing modalities, hindering fair comparison, transfer, and reproducibility. We propose the Sensing Dataset Protocol (SDP), a protocol-level specification and benchmark framework for large-scale wireless sensing. SDP defines how heterogeneous wireless signals are mapped into a unified perception data-block schema through lightweight synchronization, frequency-time alignment, and resampling, while a Canonical Polyadic-Alternating Least Squares (CP-ALS) pooling stage provides a task-agnostic representation that preserves multipath, spectral, and temporal structures. Built upon this protocol, a unified benchmark is established for detection, recognition, and vital-sign estimation with consistent preprocessing, training, and evaluation. Experiments under the cross-user split demonstrate that SDP significantly reduces variance (approximately 88%) across seeds while maintaining competitive accuracy and latency, confirming its value as a reproducible foundation for multi-modal and multitask sensing research.

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arXiv2025

Bruxism Recognition via Wireless Signal

Bruxism is an oromandibular movement disorder involving teeth grinding and clenching, which severely impairs sleep quality and dental health. However, its diagnosis remains challenging, as existing methods often cause discomfort or compromise user privacy. To address these limitations, we establish a contactless bruxism recognition system based on millimeter-wave radar. First, we analyzed the potential impact of the movement patterns of teeth grinding on radar echo signals. Based on this analysis, 11 features were extracted. Subsequently, using these features, we performed classification with a Random Forest model on the dataset constructed via millimeter-wave radar. Experimental results demonstrate that the proposed method achieves an accuracy of 96.1% on the test set, with precision, recall, and F1-score all remaining at a relatively high level. This study validates the effectiveness of millimeter-wave radar for SB recognition, providing a non-invasive and privacy-friendly alternative to existing recognition techniques. Future research will focus on enhancing the robustness of the method across diverse populations and environments, as well as striving to mitigate the interference of other facial micro-movements on teeth grinding recognition.

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arXiv2025

Joint Sensing, Communication, and Computation for Vertical Federated Edge Learning in Edge Perception Network

Combining wireless sensing and edge intelligence, edge perception networks enable intelligent data collection and processing at the network edge. However, traditional sample partition based horizontal federated edge learning struggles to effectively fuse complementary multiview information from distributed devices. To address this limitation, we propose a vertical federated edge learning (VFEEL) framework tailored for feature-partitioned sensing data. In this paper, we consider an integrated sensing, communication, and computation-enabled edge perception network, where multiple edge devices utilize wireless signals to sense environmental information for updating their local models, and the edge server aggregates feature embeddings via over-the-air computation for global model training. First, we analyze the convergence behavior of the ISCC-enabled VFEEL in terms of the loss function degradation in the presence of wireless sensing noise and aggregation distortions during AirComp.

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arXiv2025

Predictive Beamforming in Low-Altitude Wireless Networks: A Cross-Attention Approach

Accurate beam prediction is essential for maintaining reliable links and high spectral efficiency in dynamic low-altitude wireless networks. However, existing approaches often fail to capture the deep correlations across heterogeneous sensing modalities, limiting their adaptability in complex three-dimensional environments. To overcome these challenges, we propose a multi-modal predictive beamforming method based on a cross-attention fusion mechanism that jointly leverages visual and structured sensor data. The proposed model utilizes a Convolutional Neural Network (CNN) to learn multi-scale spatial feature hierarchies from visual images and a Transformer encoder to capture cross-dimensional dependencies within sensor data. Then, a cross-attention fusion module is introduced to integrate complementary information between the two modalities, generating a unified and discriminative representation for accurate beam prediction. Through experimental evaluations conducted on a real-world dataset, our method reaches 79.7% Top-1 accuracy and 99.3% Top-3 accuracy, surpassing the 3D ResNet-Transformer baseline by 4.4%-23.2% across Top-1 to Top-5 metrics. These results verify that multi-modal cross-attention fusion is effective for intelligent beam selection in dynamic low-altitude wireless networks.

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arXiv2025

Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach

Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN), particularly when terrestrial networks are unavailable. In such scenarios, the environmental topology is typically unknown; hence, designing efficient and safe UAV trajectories is essential yet challenging. To address this, we propose a digital twin (DT)-assisted training and deployment framework. In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs). These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety. Based on this framework, we further develop a trajectory design scheme that integrates simulated annealing for efficient user scheduling with the twin-delayed deep deterministic policy gradient algorithm for continuous trajectory design, aiming to minimize mission completion time while ensuring obstacle avoidance. Simulation results demonstrate that the proposed approach achieves faster convergence, higher flight safety, and shorter mission completion time compared with baseline methods, providing a robust and efficient solution for LAWN deployment in unknown environments.

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arXiv2025

Integrated Sensing and Communication: Towards Multifunctional Perceptive Network

The capacity-maximization design philosophy has driven the growth of wireless networks for decades. However, with the slowdown in recent data traffic demand, the mobile industry can no longer rely solely on communication services to sustain development. In response, Integrated Sensing and Communications (ISAC) has emerged as a transformative solution, embedding sensing capabilities into communication networks to enable multifunctional wireless systems. This paradigm shift expands the role of networks from sole data transmission to versatile platforms supporting diverse applications. In this review, we provide a bird's-eye view of ISAC for new researchers, highlighting key challenges, opportunities, and application scenarios to guide future exploration in this field.

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arXiv2025

The Role of ISAC in 6G Networks: Enabling Next-Generation Wireless Systems

The commencement of the sixth-generation (6G) wireless networks represents a fundamental shift in the integration of communication and sensing technologies to support next-generation applications. Integrated sensing and communication (ISAC) is a key concept in this evolution, enabling end-to-end support for both communication and sensing within a unified framework. It enhances spectrum efficiency, reduces latency, and supports diverse use cases, including smart cities, autonomous systems, and perceptive environments. This tutorial provides a comprehensive overview of ISAC's role in 6G networks, beginning with its evolution since 5G and the technical drivers behind its adoption. Core principles and system variations of ISAC are introduced, followed by an in-depth discussion of the enabling technologies that facilitate its practical deployment. The paper further analyzes current research directions to highlight key challenges, open issues, and emerging trends. Design insights and recommendations are also presented to support future development and implementation. This work ultimately tries to address three central questions: Why is ISAC essential for 6G? What innovations does it bring? How will it shape the future of wireless communication?

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arXiv2025

Low-Altitude Wireless Networks: A Comprehensive Survey

The rapid development of the low-altitude economy has imposed unprecedented demands on wireless infrastructure to accommodate large-scale drone deployments and facilitate intelligent services in dynamic airspace environments. However, unlocking its full potential in practical applications presents significant challenges. Traditional aerial systems predominantly focus on air-ground communication services, often neglecting the integration of sensing, computation, control, and energy-delivering functions, which hinders the ability to meet diverse mission-critical demands. Besides, the absence of systematic low-altitude airspace planning and management exacerbates issues regarding dynamic interference in three-dimensional space, coverage instability, and scalability. To overcome these challenges, a comprehensive framework, termed low-altitude wireless network (LAWN), has emerged to seamlessly integrate communication, sensing, computation, control, and air traffic management into a unified design. This article provides a comprehensive overview of LAWN systems, introducing LAWN system fundamentals and the evolution of functional designs. Subsequently, we delve into performance evaluation metrics and review critical concerns surrounding privacy and security in the open-air network environment. Finally, we present the cutting-edge developments in airspace structuring and air traffic management, providing insights to facilitate the practical deployment of LAWNs.

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arXiv2025

Hierarchical Low-Altitude Wireless Network Empowered Air Traffic Management

As the increasing development of low-altitude aircrafts, the rational design of low-altitude networks directly impacts the aerial safety and resource utilization. To address the challenges of environmental complexity and aircraft diversity in the traffic management, we propose a hierarchical low-altitude wireless network (HLWN) framework. Empowered by the threedimensional spatial discretization and integrated wireless monitoring mechanisms in HLWN, we design low-altitude air corridors to guarantee safe operation and optimization. Besides, we develop the multi-dimensional flight risk assessment through conflict detection and probabilistic collision analysis, facilitating dynamic collision avoidance for heterogeneous aircrafts. Finally, the open issues and future directions are investigated to provide insights into HLAN development.

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