arXiv subjects

Fuhui Zhou

Publications and source records attributed to Fuhui Zhou.

At least 19 recordsLinked to original sources

arXiv2026

Recursively Self-Improving Electromagnetic Spectrum Agents: Architecture and Operation

Electromagnetic spectrum agents (ESAs) must contend with changing environments and task requirements during sustained autonomous operation, yet their existing capabilities may not remain suitable for new operating conditions. This article proposes recursively self-improving electromagnetic spectrum agents (RSI-ESAs) that use operational feedback to update their capabilities and build on prior improvements. We present their architecture and editable components, and describe a self-improvement process encompassing problem diagnosis, candidate generation, validation, and inheritance. By retaining effective task versions and using modification records and diagnostic knowledge to guide subsequent updates, RSI-ESAs can accumulate task capabilities and improvement experience across successive rounds. A shared-spectrum scheduling case study illustrates improved task performance and capability accumulation over multiple improvement rounds under the tested conditions. Finally, we discuss open challenges for broader tasks and long-term operation, outlining research directions for the continued capability development of ESAs.

cs.MA↗

arXiv2026

Low-Altitude Agentic Networks: From UAV Connectivity to Workflow-Driven Capability Orchestration

Low-altitude platforms are evolving from connected sensing nodes into autonomous agents capable of bounded task interpretation, skill invocation, and local decision making. Increasing the intelligence of individual unmanned aerial vehicles (UAVs), however, does not resolve the organization problem that arises when mission workflows depend on capabilities distributed across multiple platforms and provider feasibility changes with network, resource, and mobility conditions. We propose low-altitude agentic networking as a task-level networking paradigm and develop a reference architecture that separates workflow logic from executor identity. Within a validated domain-specific workflow space, each ready operation is translated into a capability requirement and dynamically bound to an agent that remains feasible under current conditions. Task contracts and structured evidence govern workflow execution, while routine provider and link changes are handled through workflow-preserving local adaptation before high-level repair is invoked. A trace-calibrated discrete-event case study identifies a bounded operating region: conventional organization remains effective for simple workflows under stable conditions, whereas agentic networking becomes beneficial when workflow dependencies are tightly coupled, capability feasibility changes during execution, and viable local recovery paths remain available.

eess.IV↗

arXiv2026

RF-Agent: Hierarchical Language-Agent Control for Instruction-Conditioned Active Spectrum Sensing

Active radio-frequency (RF) sensing must acquire evidence under receiver limits while following confirmation, recovery, and stopping instructions. \newhl{We propose RF-Agent, a closed-loop architecture combining language supervision, episode memory, deterministic RF execution, perception feedback, and evidence-based reporting.} An independent auditor checks trajectory compliance. We derive acquisition and supervisory-request bounds, characterize full-history token complexity, and bound joint success by output validity and evidence capacity. \newhl{ActiveRF-AD tests instruction changes at fixed states and evaluates joint success, requiring compliance and RF correctness in the same episode.} \newhl{Training on different instructions at shared sensing states raises counterfactual-pair accuracy from 24.03\% to 96.22\% and Core joint success from 65.13\% to 72.12\%. Of the 6.99-point joint-success gain, 6.73 points reflect a decrease in the fraction of all episodes that are RF-correct but noncompliant.} \newhl{At Core budget three, paired-training hierarchical control achieves 66.60\% joint success versus 9.17\% for direct-action control.} \newhl{In a separate comparison at the same budget, RF-Agent improves joint success by 2.31 points and reduces RF acquisitions by 12.3\% relative to an initial-state planner, at increased inference cost.} Cross-backbone diagnostics cover three model families. These results show how instruction-responsive control improves task completion beyond what RF accuracy alone reveals.

eess.SP↗

arXiv2026

RASR: Range-Aware Scale Recovery for Metric UAV Navigation

A central challenge in image-goal UAV navigation under Global Navigation Satellite System (GNSS) denial is estimating metric distance and heading between current and goal views. Dense pairwise geometry models capture relative scene structure, but without a calibrated metric scale, they cannot directly provide reliable distance estimates for navigation. Although global scale calibration corrects the dominant scale bias, the remaining errors vary systematically with distance. In this paper, Range-Aware Scale Recovery (RASR) is proposed, which complements global scale calibration with range-aware residual correction. RASR encodes pairwise geometry extracted by a frozen Matching And Stereo 3D Reconstruction (MASt3R) backbone as a compact descriptor and separates the scale-recovery core from task-specific command calibration. On the official online evaluation of the UAVs in Multimedia 2026 PairUAV challenge, RASR achieved a total error of 0.003189, achieving a lower total error than global scale calibration alone. The results demonstrate that range-aware residual correction improves metric distance estimation beyond global scale calibration. Code and materials are available at https://github.com/lht-research/rasr-pairuav.

cs.RO↗

arXiv2026

Sensing-Aided Channel Estimation for Near-Field MIMO ISAC Systems via Cross-Attention Transformer

Near-field integrated sensing and communication (ISAC) can deliver the high spatial resolution and transmission capability with the shared spectrum and hardware. Due to the partial overlap between communication scatterers and radar targets, the sensing information can provide valuable priors to enhance the channel estimation while fusing the two heterogeneous modalities remain challenging. To address this problem, a Cross-Attention Transformer based Channel Estimation Neural Network (CAT-CENet) is developed, which includes a communication pilot branch generating the the Key and Value features and a sensing information branch generating the Query feature. By elaborating the three-module structure, CAT-CENet can focus on features of overlapped targets automatically without need of identifying them in advance. The modality contribution is theoretically analyzed based on the Shapley value to verify the cross-attention gain achieved by CAT-CENet. Simulation results show that CAT-CENet outperforms the state-of-the-art schemes, especially with the higher overlapping proportion, and is robust to the model pruning.

eess.SP↗

arXiv2026

Synergetic Empowerment: Wireless Communications Meets Embodied Intelligence

Wireless communication is evolving into an agent era, where large-scale agents with inherent embodied intelligence are not just users but active participants. The perfect combination of wireless communication and embodied intelligence can achieve a synergetic empowerment and greatly facilitate the development of agent communication. An overview of this synergetic empowerment is presented, framing it as a co-evolutionary process that transforms wireless communication from a simple utility into the digital nervous system of a collective intelligence, while simultaneously elevating isolated agents into a unified superorganism with emergent capabilities far exceeding individual contributions. Moreover, we elaborate how embodied intelligence and wireless communication mutually benefit each other through the lens of the perception-cognition-execution (PCE) loop, revealing a fundamental duality where each PCE stage both challenges network capacity and creates unprecedented opportunities for system-wide optimization. Furthermore, critical open issues and future research directions are identified.

cs.NI↗

arXiv2026

Hierarchical Observe-Orient-Decide-Act Enabled UAV Swarms in Uncertain Environments: Frameworks, Potentials, and Challenges

Unmanned aerial vehicle (UAV) swarms are increasingly explored for their potentials in various applications such as surveillance, disaster response, and military. However, UAV swarms face significant challenges of implementing effective and rapid decisions under dynamic and uncertain environments. The traditional decision-making frameworks, mainly relying on centralized control and rigid architectures, are limited by their adaptability and scalability especially in complex environments. To overcome these challenges, in this paper, we propose a hierarchical Observe-Orient-Decide-Act (H-OODA) loop based framework for the UAV swarm operation in uncertain environments, which is implemented by embedding the classical OODA loop across the cloud-edge-terminal layers, and leveraging the network function virtualization (NFV) technology to provide flexible and scalable decision-making functions. In addition, based on the proposed H-OODA framework, we joint autonomous decision-making and cooperative control to enhance the adaptability and efficiency of UAV swarms. Furthermore, we present some typical case studies to verify the improvement and efficiency of the proposed framework. Finally, the potential challenges and possible directions are analyzed to provide insights for the future H-OODA enabled UAV swarms.

cs.DC↗

arXiv2026

Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks

Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59\% and improves the TSR by 29\% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.

cs.NI↗

arXiv2026

Embodied Intelligent Spectrum Management: A New Paradigm for Dynamic Spectrum Access

Wireless communication is evolving into an agent era, where numerous intelligent agents equipped with perception, reasoning, and interaction capabilities will operate in highly dynamic wireless environments. To complete diverse complex tasks, agent communication will play a critical role, which enables autonomous information exchange with external tools, services, and other agents. This trendy movement will dramatically increase spectrum demand and result in unprecedented challenges for spectrum management. However, current spectrum management paradigms, including static spectrum allocation and intelligent management, lack the flexibility and generalization to accommodate the dynamic and heterogeneous demands of agent communication. The recent advancements in embodied intelligence (EI) bring a promising solution, and this article will provide our vision of an emerging embodied intelligent spectrum management (EISM) paradigm. We start with an architecture for EISM, elaborating its key enabling technologies. Then, a prototype platform is presented to demonstrate the advantages of EISM. Finally, key challenges and open issues are outlined to facilitate future research in this emerging field.

cs.NI↗

arXiv2025

Knowledge-Driven 3D Semantic Spectrum Map: KE-VQ-Transformer Based UAV Semantic Communication and Map Completion

Artificial intelligence (AI)-native three-dimensional (3D) spectrum maps are crucial in spectrum monitoring for intelligent communication networks. However, it is challenging to obtain and transmit 3D spectrum maps in a spectrum-efficient, computation-efficient, and AI-driven manner, especially under complex communication environments and sparse sampling data. In this paper, we consider practical air-to-ground semantic communications for spectrum map completion, where the unmanned aerial vehicle (UAV) measures the spectrum at spatial points and extracts the spectrum semantics, which are then utilized to complete spectrum maps at the ground device. Since statistical machine learning can easily be misled by superficial data correlations with the lack of interpretability, we propose a novel knowledge-enhanced semantic spectrum map completion framework with two expert knowledge-driven constraints from physical signal propagation models. This framework can capture the real-world physics and avoid getting stuck in the mindset of superficial data distributions. Furthermore, a knowledge-enhanced vector-quantized Transformer (KE-VQ-Transformer) based multi-scale low-complex intelligent completion approach is proposed, where the sparse window is applied to avoid ultra-large 3D attention computation, and the multi-scale design improves the completion performance. The knowledge-enhanced mean square error (KMSE) and root KMSE (RKMSE) are introduced as novel metrics for semantic spectrum map completion that jointly consider the numerical precision and physical consistency with the signal propagation model, based on which a joint offline and online training method is developed with supervised and unsupervised knowledge loss. The simulation demonstrates that our proposed scheme outperforms the state-of-the-art benchmark schemes in terms of RKMSE.

cs.IT↗

arXiv2025

SpectrumFM: A Foundation Model for Intelligent Spectrum Management

Intelligent spectrum management is crucial for improving spectrum efficiency and achieving secure utilization of spectrum resources. However, existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization, particularly in the complex and dynamic spectrum environments. To address these challenges, this paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management. SpectrumFM features an innovative encoder architecture that synergistically exploits the convolutional neural networks and the multi-head self-attention mechanisms to enhance feature extraction and enable robust representation learning. The model is pre-trained via two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, which leverage large-scale in-phase and quadrature (IQ) data to achieve comprehensive and transferable spectrum representations. Furthermore, a parameter-efficient fine-tuning strategy is proposed to enable SpectrumFM to adapt to various downstream spectrum management tasks, including automatic modulation classification (AMC), wireless technology classification (WTC), spectrum sensing (SS), and anomaly detection (AD). Extensive experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed, consistently outperforming conventional methods across multiple benchmarks. Specifically, SpectrumFM improves AMC accuracy by up to 12.1% and WTC accuracy by 9.3%, achieves an area under the curve (AUC) of 0.97 in SS at -4 dB signal-to-noise ratio (SNR), and enhances AD performance by over 10%.

eess.SP↗

arXiv2025

Which Channel in 6G, Low-rank or Full-rank, more needs RIS from a Perspective of DoF?

Reconfigurable intelligent surface (RIS), as an efficient tool to improve receive signal-to-noise ratio, extend coverage and create more spatial diversity, is viewed as a most promising technique for the future wireless networks like 6G. As you know, RIS is very suitable for a special wireless scenario with wireless link between BS and users being completely blocked, i.e., no link. In this paper, we extend its applications to a general scenario, i.e., rank-deficient channel, particularly some extremely low-rank ones such as no link, and line-of-sight (LoS, rank-one). Actually, there are several potential important low-rank applications like low-altitude, satellite, UAV, marine, and deep-space communications. In such a situation, it is found that RIS may make a dramatic degrees of freedom (DoF) enhancement over no RIS. By using a distributed RISs placement, the DoF of channel from BS to user in LoS channel may be even boosted from a low-rank like 0/1 to full-rank. This will achieve an extremely rate improvement via spatial parallel multiple-stream transmission from BS to user. In this paper, we present a complete review of making an in-depth discussion on DoF effect of RIS.

eess.SP↗

arXiv2025

Sensing-enabled Secure Rotatable Array System Enhanced by Multi-Layer Transmitting RIS

Programmable metasurfaces and adjustable antennas are promising technologies. The security of a rotatable array system is investigated in this paper. A dual-base-station (BS) architecture is adopted, in which the BSs collaboratively perform integrated sensing of the eavesdropper (the target) and communication tasks. To address the security challenge when the sensing target is located on the main communication link, the problem of maximizing the secrecy rate (SR) under sensing signal-to-interference-plus-noise ratio requirements and discrete constraints is formulated. This problem involves the joint optimization of the array pose, the antenna distribution on the array surface, the multi-layer transmitting RIS phase matrices, and the beamforming matrices, which is non-convex. To solve this challenge, an two-stage online algorithm based on the generalized Rayleigh quotient and an offline algorithm based on the Multi-Agent Deep Deterministic Policy Gradient are proposed. Simulation results validate the effectiveness of the proposed algorithms. Compared to conventional schemes without array pose adjustment, the proposed approach achieves approximately 22\% improvement in SR. Furthermore, array rotation provides higher performance gains than position changes.

eess.SP↗

arXiv2025

RaLD: Generating High-Resolution 3D Radar Point Clouds with Latent Diffusion

Millimeter-wave radar offers a promising sensing modality for autonomous systems thanks to its robustness in adverse conditions and low cost. However, its utility is significantly limited by the sparsity and low resolution of radar point clouds, which poses challenges for tasks requiring dense and accurate 3D perception. Despite that recent efforts have shown great potential by exploring generative approaches to address this issue, they often rely on dense voxel representations that are inefficient and struggle to preserve structural detail. To fill this gap, we make the key observation that latent diffusion models (LDMs), though successful in other modalities, have not been effectively leveraged for radar-based 3D generation due to a lack of compatible representations and conditioning strategies. We introduce RaLD, a framework that bridges this gap by integrating scene-level frustum-based LiDAR autoencoding, order-invariant latent representations, and direct radar spectrum conditioning. These insights lead to a more compact and expressive generation process. Experiments show that RaLD produces dense and accurate 3D point clouds from raw radar spectrums, offering a promising solution for robust perception in challenging environments.

cs.CV↗

arXiv2025

FSOS-AMC: Few-Shot Open-Set Learning for Automatic Modulation Classification Over Multipath Fading Channels

Automatic modulation classification (AMC) plays a vital role in advancing future wireless communication networks. Although deep learning (DL)-based AMC frameworks have demonstrated remarkable classification capabilities, they typically require large-scale training datasets and assume consistent class distributions between training and testing data-prerequisites that prove challenging in few-shot and open-set scenarios. To address these limitations, we propose a novel few-shot open-set AMC (FSOS-AMC) framework that integrates a multisequence multiscale attention network (MS-MSANet), meta-prototype training, and a modular open-set classifier. The MS-MSANet extracts features from multisequence input signals, while meta-prototype training optimizes both the feature extractor and the modular open-set classifier, which can effectively categorize testing data into known modulation types or identify potential unknown modulations. Extensive simulation results demonstrate that our FSOS-AMC framework achieves superior performance in few-shot open-set scenarios compared to state-of-the-art methods. Specifically, the framework exhibits higher classification accuracy for both known and unknown modulations, as validated by improved accuracy and area under the receiver operating characteristic curve (AUROC) metrics. Moreover, the proposed framework demonstrates remarkable robustness under challenging low signal-to-noise ratio (SNR) conditions, significantly outperforming existing approaches.

eess.SP↗

arXiv2025

Distributed Multi-Task Learning for Joint Wireless Signal Enhancement and Recognition

Wireless signal recognition (WSR) is crucial in modern and future wireless communication networks since it aims to identify the properties of the received signal in a no-collaborative manner. However, it is challenging to accurately classify signals in low signal-to-noise ratio (SNR) conditions and distributed network settings. In this paper, we propose a novel distributed multi-task learning framework for joint wireless signal enhancement and recognition (WSER), addressing the crucial need for non-collaborative signal identification in modern wireless networks. Our approach integrates a wireless signal enhancement and recognition network (WSERNet) with FedProx+, an enhanced federated learning algorithm designed for heterogeneous data distributions. Specifically, WSERNet leverages an asymmetric convolution block (ACBlock) to capture long-range dependencies in the input signal and improve the performance of the deep learning model. FedProx+ introduces a proximal term to the loss function to encourage the model updates to be closer to the previous model, enhancing the convergence speed and robustness of federated learning. Extensive experiments demonstrate the effectiveness of the proposed framework for joint WSER, achieving superior performance compared to state-of-the-art methods under both centralized and distributed settings including independent and identically distributed (IID) and non-IID data distributions.

eess.SP↗

arXiv2025

Explainable Deep Learning Based Adversarial Defense for Automatic Modulation Classification

Deep learning (DL) has been widely applied to enhance automatic modulation classification (AMC). However, the elaborate AMC neural networks are susceptible to various adversarial attacks, which are challenging to handle due to the generalization capability and computational cost. In this article, an explainable DL based defense scheme, called SHapley Additive exPlanation enhanced Adversarial Fine-Tuning (SHAP-AFT), is developed in the perspective of disclosing the attacking impact on the AMC network. By introducing the concept of cognitive negative information, the motivation of using SHAP for defense is theoretically analyzed first. The proposed scheme includes three stages, i.e., the attack detection, the information importance evaluation, and the AFT. The first stage indicates the existence of the attack. The second stage evaluates contributions of the received data and removes those data positions using negative Shapley values corresponding to the dominating negative information caused by the attack. Then the AMC network is fine-tuned based on adversarial adaptation samples using the refined received data pattern. Simulation results show the effectiveness of the Shapley value as the key indicator as well as the superior defense performance of the proposed SHAP-AFT scheme in face of different attack types and intensities.

eess.SP↗

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.

cs.NI↗