arXiv subjects

Qihui Wu

Publications and source records attributed to Qihui Wu.

At least 19 recordsLinked to original sources

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

Mission Efficiency Optimization in Low-Altitude Economy: Adaptive Power Allocation for Coordinating Heterogeneous Aircraft Swarms

With the rapid development of the low-altitude economy, low-altitude operations are booming, where complex missions require collaborative efforts among multiple heterogeneous low-altitude aircrafts (LAAs). Specifically, different LAAs assume distinct roles: some for sensing, some for communication, some for computing, and others for mission execution, together forming a sensing-communication-computing-control (SC3) closed loop, akin to a reflex arc. To enable efficient coordination in such multi-LAA swarms, we introduce the concept of operational-capability entropy (OCE) to quantify the effective work capability of operational LAAs. Accordingly, by jointly considering heterogeneous OCE and channel conditions among LAAs, we formulate the power allocation problem with the goal of minimizing the linear quadratic regulator (LQR) cost, which serves as a metric for mission efficiency. The resulting complex optimization problem is decomposed into two convex subproblems that are solved iteratively, with closed-form solutions derived for each. Simulation results demonstrate that the proposed mission-oriented adaptive power allocation scheme significantly outperforms traditional ones.

cs.IT↗

arXiv2026

Joint Spectrum and Airspace Resource Optimization for Low-altitude Wireless Network

Low-altitude wireless networks have emerged as a promising platform for enabling the safe and efficient operation of unmanned aerial vehicles (UAVs). However, due to the limited spectrum and airspace resources, it is challenging to efficiently accomplish UAV flight tasks without collisions. In this paper, we propose a sequential framework with two coupled stages that coordinates spectrum allocation and airspace planning to construct efficient low-altitude air corridors. Specifically, the low-altitude airspace is discretized into a set of digital grids, where obstacles are modeled as impermeable units. Then, we formulate an optimization problem to minimize the total traversal cost of air corridors, which is challenging to solve due to the tight coupling between spectrum allocation and path planning.Therefore, we first design a constrained Vickrey-Clarke-Groves (VCG) ascending auction mechanism to allocate the spectrum resources. Then, we propose a joint spectrum and airspace resource allocation algorithm to minimize the total traversal cost of air corridors. Finally, simulation results show that the proposed algorithms achieve lower total costs than the baseline algorithms.

eess.SY↗

arXiv2026

Radio Map Updating from Streaming Spectrum Measurements via Memory-Based Online Gaussian Processes

Radio maps, which estimate spatial radio-frequency characteristics from spectrum measurements, are essential for applications such as spectrum management and network planning. With the continuous arrival of spectrum measurements, conventional batch processing methods for radio map reconstruction become computationally prohibitive, as they require reprocessing all accumulated measurements for each radio map update. To address this, we propose a memory-based online sparse variational Gaussian process (M-OSVGP) method that efficiently updates radio maps from streaming spectrum measurements. Our method employs sparse variational inference and updates the posterior online by minimizing a hybrid objective that integrates newly received measurements and a memory subset of previous ones to mitigate catastrophic forgetting. To further improve posterior approximation as measurements accumulate over spatially diverse regions, we extend M-OSVGP with a grid-assisted online inducing point selection (GOIPS) algorithm. GOIPS dynamically adapts the number and locations of inducing points based on measurement density and spatial correlation, providing a more informative inducing set while maintaining computational efficiency. Extensive simulations demonstrate the effectiveness of our proposed methods in reconstruction accuracy, computational efficiency, and uncertainty quantification, compared to existing batch and online baselines across various scenarios.

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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

A Blockchain-Based Trust Framework for Resilient Cross-Domain UAV Service Orchestration

Unmanned aerial vehicle (UAV) networks are increasingly deployed for complex missions, including disaster response, intelligent logistics, and environmental monitoring. These missions generally require coordinated collaboration among multiple UAVs across distinct administrative domains. To support such cross-domain cooperation, service function chains (SFCs) are constructed, where complex workflows are decomposed into ordered service functions assigned to appropriate UAVs along the mission path. However, it is challenging to ensure secure, trustworthy, and low-latency cross-domain SFC orchestration in identity management, authentication, and resilience to node failures. To address these issues, this paper proposes a consortium blockchain-based trust architecture for cross-domain decentralized identity verification, auditable task execution, and dynamic service-aware orchestrator selection. The framework employs a hierarchical four-phase cross-domain authentication protocol covering the credential pre-verification, intra-domain execution, secure relay, and audit logging. The use case analysis confirms that the proposed framework achieves substantial reductions in authentication latency and significant improvements in system throughput against centralized and static schemes. The open challenges in scalability, adaptive trust assessment, interoperability, and energy efficiency are discussed, thereby providing directions for future researches on secure and efficient cross-domain UAV service orchestration.

cs.CR↗

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

Prompt-Driven Low-Altitude Edge Intelligence: Modular Agents and Generative Reasoning

The large artificial intelligence models (LAMs) show strong capabilities in perception, reasoning, and multi-modal understanding, and can enable advanced capabilities in low-altitude edge intelligence. However, the deployment of LAMs at the edge remains constrained by some fundamental limitations. First, tasks are rigidly tied to specific models, limiting the flexibility. Besides, the computational and memory demands of full-scale LAMs exceed the capacity of most edge devices. Moreover, the current inference pipelines are typically static, making it difficult to respond to real-time changes of tasks. To address these challenges, we propose a prompt-to-agent edge cognition framework (P2AECF), enabling the flexible, efficient, and adaptive edge intelligence. Specifically, P2AECF transforms high-level semantic prompts into executable reasoning workflows through three key mechanisms. First, the prompt-defined cognition parses task intent into abstract and model-agnostic representations. Second, the agent-based modular execution instantiates these tasks using lightweight and reusable cognitive agents dynamically selected based on current resource conditions. Third, the diffusion-controlled inference planning adaptively constructs and refines execution strategies by incorporating runtime feedback and system context. In addition, we illustrate the framework through a representative low-altitude intelligent network use case, showing its ability to deliver adaptive, modular, and scalable edge intelligence for real-time low-altitude aerial collaborations.

cs.AI↗

arXiv2026

A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction

The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base Stations (BSs), these patterns are typically proprietary and not readily accessible. Operational parameters of BSs, which inherently contain beam information, offer an opportunity for data-driven low-altitude coverage prediction. However, collecting extensive low-altitude road test data is cost-prohibitive, often yielding only sparse samples per BS. This scarcity results in two primary challenges: imbalanced feature sampling due to limited variability in high-dimensional operational parameters against the backdrop of substantial changes in low-dimensional sampling locations, and diminished generalizability stemming from insufficient data samples. To overcome these obstacles, we introduce a dual strategy comprising expert knowledge-based feature compression and disentangled representation learning. The former reduces feature space complexity by leveraging communications expertise, while the latter enhances model generalizability through the integration of propagation models and distinct subnetworks that capture and aggregate the semantic representations of latent features. Experimental evaluation confirms the efficacy of our framework, yielding a 7% reduction in error compared to the best baseline algorithm. Real-network validations further attest to its reliability, achieving practical prediction accuracy with MAE errors at the 5dB level.

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↗

arXiv2026

IGAA: Intent-Driven General Agentic AI for Edge Services Scheduling using Generative Meta Learning

Agentic AI (AAI), which extends Large Language Models with enhanced reasoning capabilities, has emerged as a promising paradigm for autonomous edge service scheduling. However, user mobility creates highly dynamic service demands in edge networks, and existing service scheduling agents often lack generalization capabilities for new scenarios. Therefore, this paper proposes a novel Intent-Driven General Agentic AI (IGAA) framework. Leveraging a meta-learning paradigm, IGAA enables AAI to continuously learn from prior service scheduling experiences to achieve generalized scheduling capabilities. Particularly, IGAA incorporates three core mechanisms. First, we design a Network-Service-Intent matrix mapping method to allow agents to simulate novel scenarios and generate training datasets. Second, we present an easy-to-hard generalization learning scheme with two customized algorithms, namely Resource Causal Effect-aware Transfer Learning (RCETL) and Action Potential Optimality-aware Transfer Learning (APOTL). These algorithms help IGAA adapt to new scenarios. Furthermore, to prevent catastrophic forgetting during continual IGAA learning, we propose a Generative Intent Replay (GIR) mechanism that synthesizes historical service data to consolidate prior capabilities. Finally, to mitigate the effect of LLM hallucinations on scenario simulation, we incorporate a scenario evaluation and correction model to guide agents in generating rational scenarios and datasets. Extensive experiments demonstrate IGAA's strong generalization and scalability. Specifically, IGAA enables rapid adaptation by transferring learned policies to analogous new ones, such as applying latency-sensitive patterns from real-time computing to optimize novel Internet of Vehicles (IoV) services. Compared to scenario-specific methods, IGAA maintains the intent-satisfaction rate gap within 3.81%.

cs.NI↗

arXiv2026

SDN-Blockchain Based Security Routing for UAV Communication via Reinforcement Learning

The unmanned aerial vehicle (UAV) network plays important roles in emergency communications. However, it is challenging to design reliable routing strategies that ensure low latency, energy efficiency, and security in the dynamic and attack-prone environments. To this end, we design a secure routing architecture integrating software-defined networking (SDN) for centralized control and blockchain for tamper-proof trust management. In particular, a novel security degree metric is introduced to quantify the UAV trustworthiness. Based on this architecture, we propose a beam search-proximal policy optimization (BSPPO) algorithm, where beam search (BS) pre-screens the high-security candidate paths, and proximal policy optimization (PPO) performs hop-by-hop routing decisions to support dynamic rerouting upon attack detections. Finally, extensive simulations under varying attack densities, packet sizes, and rerouting events demonstrate that BSPPO outperforms PPO, BS-Q learning, and BS-actor critic in terms of delay, energy consumption, and transmission success rate, showing the outstanding robustness and adaptability.

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↗