arXiv subjects

Ziye Jia

Publications and source records attributed to Ziye Jia.

At least 19 recordsLinked to original sources

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

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.

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

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

Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL

The low-altitude intelligent networks (LAINs) emerge as a promising architecture for delivering low-latency and energy-efficient edge intelligence in dynamic and infrastructure-limited environments. By integrating unmanned aerial vehicles (UAVs), aerial base stations, and terrestrial base stations, LAINs can support mission-critical applications such as disaster response, environmental monitoring, and real-time sensing. However, these systems face key challenges, including energy-constrained UAVs, stochastic task arrivals, and heterogeneous computing resources. To address these issues, we propose an integrated air-ground collaborative network and formulate a time-dependent integer nonlinear programming problem that jointly optimizes UAV trajectory planning and task offloading decisions. The problem is challenging to solve due to temporal coupling among decision variables. Therefore, we design a hierarchical learning framework with two timescales. At the large timescale, a Vickrey-Clarke-Groves auction mechanism enables the energy-aware and incentive-compatible trajectory assignment. At the small timescale, we propose the diffusion-heterogeneous-agent proximal policy optimization, a generative multi-agent reinforcement learning algorithm that embeds latent diffusion models into actor networks. Each UAV samples actions from a Gaussian prior and refines them via observation-conditioned denoising, enhancing adaptability and policy diversity. Extensive simulations show that our framework outperforms baselines in energy efficiency, task success rate, and convergence performance.

cs.LG↗

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.

eess.SP↗

arXiv2025

DRO-Based Computation Offloading and Trajectory Design for Low-Altitude Networks

The low-altitude networks (LANs) integrating unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) have become a promising solution for the rising computation demands. However, the uncertain task sizes and high mobility of UAVs pose great challenges to guarantee the quality of service. To address these issues, we propose an LAN architecture where UAVs and HAPs collaboratively provide computation offloading for ground users. Moreover, the uncertainty sets are constructed to characterize the uncertain task size, and a distributionally robust optimization problem is formulated to minimize the worst-case delay by jointly optimizing the offloading decisions and UAV trajectories. To solve the mixed-integer min-max optimization problem, we design the distributionally robust computation offloading and trajectories optimization algorithm. Specifically, the original problem is figured out by iteratively solving the outerlayer and inner-layer problems. The convex outer-layer problem with probability distributions is solved by the optimization toolkit. As for the inner-layer mixed-integer problem, we employ the Benders decomposition. The decoupled master problem concerning the binary offloading decisions is solved by the integer solver, and UAV trajectories in the sub-problem are optimized via the successive convex approximation. Simulation results show the proposed algorithm outperforms traditional optimization methods in balancing the worst-case delay and robustness.

cs.CE↗

arXiv2025

A Lightweight Authentication and Key Agreement Protocol Design for FANET

The advancement of low-altitude intelligent networks enables unmanned aerial vehicle (UAV) interconnection via flying ad-hoc networks (FANETs), offering flexibility and decentralized coordination. However, resource constraints, dynamic topologies, and UAV operations in open environments present significant security and communication challenges. Existing multi-factor and public-key cryptography protocols are vulnerable due to their reliance on stored sensitive information, increasing the risk of exposure and compromise. This paper proposes a lightweight authentication and key agreement protocol for FANETs, integrating physical unclonable functions with dynamic credential management and lightweight cryptographic primitives. The protocol reduces computational and communication overhead while enhancing security. Security analysis confirms its resilience against various attacks, and comparative evaluations demonstrate its superiority in security, communication efficiency, and computational cost.

cs.CR↗

arXiv2025

Robust and Secure Computation Offloading and Trajectory Optimization for Multi-UAV MEC Against Aerial Eavesdropper

The unmanned aerial vehicle (UAV) based multi-access edge computing (MEC) appears as a popular paradigm to reduce task processing latency. However, the secure offloading is an important issue when occurring aerial eavesdropping. Besides, the potential uncertainties in practical applications and flexible trajectory optimizations of UAVs pose formidable challenges for realizing robust offloading. In this paper, we consider the aerial secure MEC network including ground users, service unmanned aerial vehicles (S-UAVs) integrated with edge servers, and malicious UAVs overhearing transmission links. To deal with the task computation complexities, which are characterized as uncertainties, a robust problem is formulated with chance constraints. The energy cost is minimized by optimizing the connections, trajectories of S-UAVs and offloading ratios. Then, the proposed non-linear problem is tackled via the distributionally robust optimization and conditional value-at-risk mechanism, which is further transformed into the second order cone programming forms. Moreover, we decouple the reformulated problem and design the successive convex approximation for S-UAV trajectories. The global algorithm is designed to solve the sub-problems in a block coordinate decent manner. Finally, extensive simulations and numerical analyses are conducted to verify the robustness of the proposed algorithms, with just 2\% more energy cost compared with the ideal circumstance.

cs.ET↗

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↗

arXiv2025

Dynamic Trajectory Optimization and Power Control for Hierarchical UAV Swarms in 6G Aerial Access Network

Unmanned aerial vehicles (UAVs) can serve as aerial base stations (BSs) to extend the ubiquitous connectivity for ground users (GUs) in the sixth-generation (6G) era. However, it is challenging to cooperatively deploy multiple UAV swarms in large-scale remote areas. Hence, in this paper, we propose a hierarchical UAV swarms structure for 6G aerial access networks, where the head UAVs serve as aerial BSs, and tail UAVs (T-UAVs) are responsible for relay. In detail, we jointly optimize the dynamic deployment and trajectory of UAV swarms, which is formulated as a multi-objective optimization problem (MOP) to concurrently minimize the energy consumption of UAV swarms and GUs, as well as the delay of GUs. However, the proposed MOP is a mixed integer nonlinear programming and NP-hard to solve. Therefore, we develop a K-means and Voronoi diagram based area division method, and construct Fermat points to establish connections between GUs and T-UAVs. Then, an improved non-dominated sorting whale optimization algorithm is proposed to seek Pareto optimal solutions for the transformed MOP. Finally, extensive simulations are conducted to verify the performance of proposed algorithms by comparing with baseline mechanisms, resulting in a 50% complexity reduction.

cs.NI↗

arXiv2025

Remote ID Based UAV Collision Avoidance Optimization for Low-Altitude Airspace Safety

With the rapid development of unmanned aerial vehicles (UAVs), it is paramount to ensure safe and efficient operations in open airspaces. The remote identification (Remote ID) is deemed an effective real-time UAV monitoring system by the federal aviation administration, which holds potentials for enabling inter-UAV communications. This paper deeply investigates the application of Remote ID for UAV collision avoidance while minimizing communication delays. First, we propose a Remote ID based distributed multi-UAV collision avoidance (DMUCA) framework to support the collision detection, avoidance decision-making, and trajectory recovery. Next, the average transmission delays for Remote ID messages are analyzed, incorporating the packet reception mechanisms and packet loss due to interference. The optimization problem is formulated to minimize the long-term average communication delay, where UAVs can flexibly select the Remote ID protocol to enhance the collision avoidance performance. To tackle the problem, we design a multi-agent deep Q-network based adaptive communication configuration algorithm, allowing UAVs to autonomously learn the optimal protocol configurations in dynamic environments. Finally, numerical results verify the feasibility of the proposed DMUCA framework, and the proposed mechanism can reduce the average delay by 32% compared to the fixed protocol configuration.

eess.SP↗

arXiv2025

Trusted Routing for Blockchain-Empowered UAV Networks via Multi-Agent Deep Reinforcement Learning

Due to the high flexibility and versatility, unmanned aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue.However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we characterize the routing process in a time-varying UAV network with malicious nodes. Specifically, we formulate the routing problem to minimize the total delay, which is an integer linear programming and intractable to solve. Then, to tackle the network security issue, a blockchain-based trust management mechanism (BTMM) is designed to dynamically evaluate trust values and identify low-trust UAVs. To improve traditional practical Byzantine fault tolerance algorithms in the blockchain, we propose a consensus UAV update mechanism. Besides, considering the local observability, the routing problem is reformulated into a decentralized partially observable Markov decision process. Further, a multi-agent double deep Q-network based routing algorithm is designed to minimize the total delay. Finally, simulations are conducted with attacked UAVs and numerical results show that the delay of the proposed mechanism decreases by 13.39$\%$, 12.74$\%$, and 16.6$\%$ than multi-agent proximal policy optimal algorithms, multi-agent deep Q-network algorithms, and methods without BTMM, respectively.

eess.SY↗

arXiv2025

Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning

With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility and flexible deployment. However, the existing UAV-assisted offloading strategies are insufficient in coordinating heterogeneous computing resources and adapting to dynamic network conditions. Hence, this paper proposes a dual-layer UAV-assisted edge computing architecture based on partial offloading, composed of the relay capability of high-altitude UAVs and the computing support of low-altitude UAVs. The proposed architecture enables efficient integration and coordination of heterogeneous resources. A joint optimization problem is formulated to minimize the system delay and energy consumption while ensuring the task completion rate. To solve the high-dimensional decision problem, we reformulate the problem as a Markov decision process and propose a hierarchical offloading scheme based on the soft actor-critic algorithm. The method decouples global and local decisions, where the global decisions integrate offloading ratios and trajectory planning into continuous actions, while the local scheduling is handled via designing a priority-based mechanism. Simulations are conducted and demonstrate that the proposed approach outperforms several baselines in task completion rate, system efficiency, and convergence speed, showing strong robustness and applicability in dynamic vehicular environments.

cs.LG↗

arXiv2025

Trusted Routing for Blockchain-Enabled Low-Altitude Intelligent Networks

Due to the scalability and portability, the 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 dynamic mobility, and vulnerable to security threats, which may degrade the routing performance for data transmission. Hence, how to ensure the routing stability and security of LAINs is a challenge. In this paper, we focus on the routing process in LAINs with multiple UAV clusters and propose the blockchain-enabled zero-trust architecture to manage the joining and exiting of UAVs. Furthermore, we formulate the routing problem to minimize the end-to-end (E2E) delay, which is an integer linear programming and intractable to solve. Therefore, considering the distribution of LAINs, we reformulate the routing problem into a decentralized partially observable Markov decision process. With the proposed soft hierarchical experience replay buffer, the multi-agent double deep Q-network based adaptive routing algorithm is designed. Finally, simulations are conducted and numerical results show that the total E2E delay of the proposed mechanism decreases by 22.38\% than the benchmark on average.

cs.NI↗