arXiv subjects

Linling Kuang

Publications and source records attributed to Linling Kuang.

17 recordsLinked to original sources

arXiv2026

A Consolidated Game Framework for Cooperative Defense Against Cross-Domain Cyber Attacks in Satellite-Enabled Internet of Things

As the adoption of satellite-enabled Internet of Things (IoT) continues to rise, its intricate multidomain architecture becomes increasingly susceptible to cross-domain cyber threats. Attackers can exploit compromised IoT devices, inject malicious packets into data streams aggregated at the IoT gateway for satellite backhaul, and potentially endanger the satellite network during transmission by exploiting the hardware, software, and protocol vulnerabilities. Compared to single-domain defenses, cooperative defense at the IoT devices, IoT access network, and satellite transmission network provides fine-granularity defense against cross-domain intelligent attacks. However, quantifying cross-domain impacts and tilting incentive misalignment among different participants remain significant challenges, making systematic cooperative defense development a complex task. To address this, we develop a tripartite security game framework to characterize the impacts of attacks and defense methods across both the terrestrial and satellite domains. Leveraging this game model, we devise flow pricing to optimally motivate the IoT network operator (IoT-NO) to prevent malicious packet infiltration into the satellite domain. Subsequently, we propose efficient learning algorithms enabling both the IoT-NO to ascertain their ideal flow sampling strategies and the satellite service provider (SAT-SP) to determine optimal flow pricing. The simulation results corroborate the effectiveness of the consolidated game in counteracting cross-domain cyber attacks and facilitating cooperative defense between the IoT-NO and the SAT-SP with nonaligned incentives.

cs.GT↗

arXiv2026

Diffusion-Based Noise-Adaptive Null-Space Channel Estimation for OFDM Systems

Accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems remains challenging when demodulation reference signal (DMRS) observations are sparse and noisy, and when DMRS configurations vary across deployment scenarios. This paper proposes DANCE (Diffusion-based Noise-Adaptive Null-space Channel Estimation), a diffusion-based channel estimator for OFDM systems. We formulate DMRS-aided channel estimation as a sparse linear inverse problem whose measurement operator is induced by the pilot pattern. The resulting range-null space decomposition separates the measurement-constrained range-space component from the unobserved null-space component, which is reconstructed through a learned diffusion prior. To avoid directly imposing noisy pilot samples as exact constraints, DANCE introduces a noise-adaptive posterior correction into the reverse diffusion process. The correction coefficient and the residual sampling variance are jointly calibrated according to the observation noise level, thereby reducing pilot-noise injection while retaining useful measurement information. We further design a conditional U-Net denoiser for complex-valued OFDM channel grids, where the real and imaginary components are represented as separate feature channels and downsampling is performed only along the subcarrier dimension. Simulations based on 5G NR tapped delay line (TDL) and clustered delay line (CDL) channel models show that DANCE achieves consistently lower normalized mean squared error (NMSE) than conventional estimators and diffusion-based posterior sampling methods under different signal-to-noise ratios, DMRS configurations, Doppler frequency shifts, and train-test distribution mismatches.

cs.IT↗

arXiv2026

Toward Secure Operation and Management (O&M) of Satellite Constellations: Efficiency, Resilience, and Reliability in a Network Perspective

Satellite constellations equipped with Inter-Satellite Links and onboard packet switching enable real-time Operation and Management across globally distributed satellites, but also broaden the attack surface and introduce unprecedented cybersecurity threats. Existing efforts mainly focus on cryptography for single-satellite point-to-point links, without considering constellation-level security. To address this gap, this article extends security research in two directions: from individual satellites to constellation-wide architectures, and from isolated cryptography to system-level security incorporating efficiency, resilience, and reliability. These extensions raise three key questions: how to design efficient security mechanisms for dynamic constellation topologies with adaptive onboard routing; how a constellation O&M system can recover resiliently under worst-case failures of onboard security functions; and how to improve the reliability of onboard security functions under stringent resource constraints. To address these challenges, we first construct a constellation-wide hybrid security framework that protects semantically sensitive content fields using End-to-End encryption, while safeguarding routing-related fields through Moving Target Defense. Next, we introduce a ciphered-mode and safe-mode management mechanism with an M-delayed fallback that balances recovery timeliness and exploitability. Finally, we propose security-aware routers that manage plaintext/ciphered modes and coordinate access to a shared pool of onboard cipher modules, enabling redundancy sharing across multiple endpoints and extending secure operation duration in ciphered mode. These solutions comply with existing standards defined by organizations including DVB and the CCSDS, while translating conceptual security principles into practical system-level mechanisms.

cs.CE↗

arXiv2026

YUHENG-OS: A Cloud-Native Space Cluster Operating System

As industry and academia continue to advance spaceborne computing and communication capabilities, the formation of cloud-native space clusters (CNSCs) has become an increasingly evident trend. This evolution progressively exposes the resource management challenges associated with coordinating fragmented and heterogeneous onboard resources while supporting large-scale and diverse space applications. However, directly transplanting mature terrestrial cloud-native cluster operating system paradigms into space is ineffective due to the fragmentation of spaceborne computing resources and satellite mobility, which collectively impose substantial challenges on resource awareness and orchestration. This article presents YUHENG-OS, a cloud-native space cluster operating system tailored for CNSCs. YUHENG-OS provides unified abstraction, awareness, and orchestration of heterogeneous spaceborne infrastructure, enabling cluster-wide task deployment and scheduling across distributed satellites. We introduce a four-layer system architecture and three key enabling technologies: modeling of heterogeneous resource demands for space tasks, fragmented heterogeneous resource awareness under network constraints, and matching of differentiated tasks with multidimensional heterogeneous resources under temporal dependency constraints. Evaluation results show that, compared with representative terrestrial cloud-native cluster operating systems exemplified by Kubernetes, YUHENG-OS achieves a substantially higher task completion ratio, with improvements of up to 98%. This advantage is primarily attributed to its ability to reduce resource awareness delay by 71%.

cs.NI↗

arXiv2026

STARDIS: Strategic Scheduling and Deceptive Signaling for Satellite Intrusion Detection System Deployment

Satellite communication networks operate under stringent computational constraints and are susceptible to sophisticated cyberattacks. This paper introduces a novel defense framework that decouples security optimization into ground-based analysis and onboard real-time execution. In the long-term loop, the ground segment processes historical data to estimate key statistical parameters of the task environment. Additionally, we incorporate the time-varying characteristics of satellite wireless links to account for the dynamic communication context. In the short-term loop, the satellite employs a receding horizon optimization that models dynamic task arrivals and maximizes a utility function considering detection rates and resource costs. To counter intelligent adversaries interception, we introduce a deception mechanism using Bayesian persuasion theory. By strategically manipulating the short-term action sequences in the telemetry downlink, we mislead an external attacker's beliefs. We mathematically model the attacker's optimal response under channel uncertainty and demonstrate that our framework significantly reduces attacker utility. The approach's effectiveness is formally proven using Lyapunov theory.

cs.GT↗

arXiv2025

Generative Spectrum Cartography: Unified Reconstruction and Active Sensing via Diffusion Models

High-fidelity spectrum cartography is important for spectrum monitoring and wireless situational awareness, especially in satellite-based wide-area sensing scenarios where measurements are sparse, noisy, and often low-bit quantized. In such settings, two coupled challenges arise: accurate reconstruction from severely incomplete measurements and efficient allocation of additional sensing resources under a limited sensing budget. Existing methods usually address these problems separately, and, for reconstruction, they often rely on priors that are insufficiently expressive under sparse and quantized measurements. This paper proposes Generative Spectrum Cartography (GSC), a diffusion-based posterior inference framework for spectrum cartography with uncertainty-aware active sensing. Specifically, spectrum map recovery is formulated as a Bayesian inverse problem under a learned diffusion model prior, and closed-form posterior mean updates are derived for both linear and quantized measurement models. By embedding these updates into the reverse diffusion process, GSC enables gradient-free and measurement-consistent posterior sampling without relying on computationally costly likelihood-gradient guidance. The resulting posterior samples are further used to estimate spatial uncertainty and to guide diversity-aware selection of additional measurement locations for active sensing. Experiments on simulated electromagnetic maps and a high-fidelity simulated satellite monitoring scenario show that GSC achieves higher PSNR, lower LPIPS, and more efficient sensing than representative baseline methods under sparse, noisy, and low-bit quantized measurements.

cs.IT↗

arXiv2025

Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies

Driven by the growing demand for intelligent remote sensing applications, large artificial intelligence (AI) models pre-trained on large-scale unlabeled datasets and fine-tuned for downstream tasks have significantly improved learning performance for various downstream tasks due to their generalization capabilities. However, many specific downstream tasks, such as extreme weather nowcasting (e.g., downburst and tornado), disaster monitoring, and battlefield surveillance, require real-time data processing. Traditional methods via transferring raw data to ground stations for processing often cause significant issues in terms of latency and trustworthiness. To address these challenges, satellite edge AI provides a paradigm shift from ground-based to on-board data processing by leveraging the integrated communication-and-computation capabilities in space computing power networks (Space-CPN), thereby enhancing the timeliness, effectiveness, and trustworthiness for remote sensing downstream tasks. Moreover, satellite edge large AI model (LAM) involves both the training (i.e., fine-tuning) and inference phases, where a key challenge lies in developing computation task decomposition principles to support scalable LAM deployment in resource-constrained space networks with time-varying topologies. In this article, we first propose a satellite federated fine-tuning architecture to split and deploy the modules of LAM over space and ground networks for efficient LAM fine-tuning. We then introduce a microservice-empowered satellite edge LAM inference architecture that virtualizes LAM components into lightweight microservices tailored for multi-task multimodal inference. Finally, we discuss the future directions for enhancing the efficiency and scalability of satellite edge LAM, including task-oriented communication, brain-inspired computing, and satellite edge AI network optimization.

cs.LG↗

arXiv2025

Space-Based Computing Networks: Trends, Architecture, Challenges, and Key Technologies

As one of the most promising hotspots in the 6G era, space remote sensing information networks play a key and irreplaceable role in areas such as emergency response and scientific research, and are expected to foster remote sensing data processing into the next generation of killer applications. However, due to the inability to deploy ground communication stations at scale and the limited satellite-to-ground link rate, the traditional model for transmitting space data back to ground stations faces significant challenges in terms of timeliness. To address this problem, we focus on the emerging paradigm of on-orbit space data processing, which reduces the volume of transmitted data by several orders of magnitude to enable faster task response, taking the first step toward building a space-based computing network. Specifically, we propose a hierarchical space-based computing network architecture, comprising the space-based cloud constellation system, the remote sensing constellation system, the network operation control center, the orchestration data center, and the user access portal. Each component is described in detail from a system design perspective to clarify its specific role and functionality. Next, we analyze three scientific challenges: the heterogeneous resource virtualization and state information synchronization, the matching of multi-priority tasks with multidimensional resources, and the fault detection and localization under extreme conditions. Finally, we discuss key technologies to address the aforementioned challenges and highlight promising research priorities for the future.

cs.NI↗

arXiv2025

Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks

Satellite networks are able to collect massive space information with advanced remote sensing technologies, which is essential for real-time applications such as natural disaster monitoring. However, traditional centralized processing by the ground server incurs a severe timeliness issue caused by the transmission bottleneck of raw data. To this end, Space Computing Power Networks (Space-CPN) emerges as a promising architecture to coordinate the computing capability of satellites and enable on board data processing. Nevertheless, due to the natural limitations of solar panels, satellite power system is difficult to meet the energy requirements for ever-increasing intelligent computation tasks of artificial neural networks. To tackle this issue, we propose to employ spiking neural networks (SNNs), which is supported by the neuromorphic computing architecture, for on-board data processing. The extreme sparsity in its computation enables a high energy efficiency. Furthermore, to achieve effective training of these on-board models, we put forward a decentralized neuromorphic learning framework, where a communication-efficient inter-plane model aggregation method is developed with the inspiration from RelaySum. We provide a theoretical analysis to characterize the convergence behavior of the proposed algorithm, which reveals a network diameter related convergence speed. We then formulate a minimum diameter spanning tree problem on the inter-plane connectivity topology and solve it to further improve the learning performance. Extensive experiments are conducted to evaluate the superiority of the proposed method over benchmarks.

cs.LG↗

arXiv2025

Microservice Deployment in Space Computing Power Networks via Robust Reinforcement Learning

With the growing demand for Earth observation, it is important to provide reliable real-time remote sensing inference services to meet the low-latency requirements. The Space Computing Power Network (Space-CPN) offers a promising solution by providing onboard computing and extensive coverage capabilities for real-time inference. This paper presents a remote sensing artificial intelligence applications deployment framework designed for Low Earth Orbit satellite constellations to achieve real-time inference performance. The framework employs the microservice architecture, decomposing monolithic inference tasks into reusable, independent modules to address high latency and resource heterogeneity. This distributed approach enables optimized microservice deployment, minimizing resource utilization while meeting quality of service and functional requirements. We introduce Robust Optimization to the deployment problem to address data uncertainty. Additionally, we model the Robust Optimization problem as a Partially Observable Markov Decision Process and propose a robust reinforcement learning algorithm to handle the semi-infinite Quality of Service constraints. Our approach yields sub-optimal solutions that minimize accuracy loss while maintaining acceptable computational costs. Simulation results demonstrate the effectiveness of our framework.

cs.NI↗

arXiv2024

Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint

Diffusion models have shown strong performances in solving inverse problems through posterior sampling while they suffer from errors during earlier steps. To mitigate this issue, several Decoupled Posterior Sampling methods have been recently proposed. However, the reverse process in these methods ignores measurement information, leading to errors that impede effective optimization in subsequent steps. To solve this problem, we propose Guided Decoupled Posterior Sampling (GDPS) by integrating a data consistency constraint in the reverse process. The constraint performs a smoother transition within the optimization process, facilitating a more effective convergence toward the target distribution. Furthermore, we extend our method to latent diffusion models and Tweedie's formula, demonstrating its scalability. We evaluate GDPS on the FFHQ and ImageNet datasets across various linear and nonlinear tasks under both standard and challenging conditions. Experimental results demonstrate that GDPS achieves state-of-the-art performance, improving accuracy over existing methods.

cs.LG↗

arXiv2024

Hierarchical Learning and Computing over Space-Ground Integrated Networks

Space-ground integrated networks hold great promise for providing global connectivity, particularly in remote areas where large amounts of valuable data are generated by Internet of Things (IoT) devices, but lacking terrestrial communication infrastructure. The massive data is conventionally transferred to the cloud server for centralized artificial intelligence (AI) models training, raising huge communication overhead and privacy concerns. To address this, we propose a hierarchical learning and computing framework, which leverages the lowlatency characteristic of low-earth-orbit (LEO) satellites and the global coverage of geostationary-earth-orbit (GEO) satellites, to provide global aggregation services for locally trained models on ground IoT devices. Due to the time-varying nature of satellite network topology and the energy constraints of LEO satellites, efficiently aggregating the received local models from ground devices on LEO satellites is highly challenging. By leveraging the predictability of inter-satellite connectivity, modeling the space network as a directed graph, we formulate a network energy minimization problem for model aggregation, which turns out to be a Directed Steiner Tree (DST) problem. We propose a topologyaware energy-efficient routing (TAEER) algorithm to solve the DST problem by finding a minimum spanning arborescence on a substitute directed graph. Extensive simulations under realworld space-ground integrated network settings demonstrate that the proposed TAEER algorithm significantly reduces energy consumption and outperforms benchmarks.

cs.LG↗

arXiv2023

DNFS-VNE: Deep Neuro Fuzzy System Driven Virtual Network Embedding

By decoupling substrate resources, network virtualization (NV) is a promising solution for meeting diverse demands and ensuring differentiated quality of service (QoS). In particular, virtual network embedding (VNE) is a critical enabling technology that enhances the flexibility and scalability of network deployment by addressing the coupling of Internet processes and services. However, in the existing deep neural networks (DNNs)-based works, the black-box nature DNNs limits the analysis, development, and improvement of systems. For example, in the industrial Internet of Things (IIoT), there is a conflict between decision interpretability and the opacity of DNN-based methods. In recent times, interpretable deep learning (DL) represented by deep neuro fuzzy systems (DNFS) combined with fuzzy inference has shown promising interpretability to further exploit the hidden value in the data. Motivated by this, we propose a DNFS-based VNE algorithm that aims to provide an interpretable NV scheme. Specifically, data-driven convolutional neural networks (CNNs) are used as fuzzy implication operators to compute the embedding probabilities of candidate substrate nodes through entailment operations. And, the identified fuzzy rule patterns are cached into the weights by forward computation and gradient back-propagation (BP). Moreover, the fuzzy rule base is constructed based on Mamdani-type linguistic rules using linguistic labels. In addition, the DNFS-driven five-block structure-based policy network serves as the agent for deep reinforcement learning (DRL), which optimizes VNE decision-making through interaction with the environment. Finally, the effectiveness of evaluation indicators and fuzzy rules is verified by simulation experiments.

cs.NI↗

arXiv2023

Asynchronous multi-class traffic management in wide area networks

The emergence of new applications brings multi-class traffic with diverse quality of service (QoS) requirements to wide area networks (WANs), motivating research in traffic engineering (TE). In recent years, novel centralized and hierarchical TE schemes have used heuristic or machine learning techniques to orchestrate resources in closed systems such as datacenter networks. However, these schemes suffer from long delivery delays and high control overhead when applied to general WANs. To provide low-delay services, this paper proposes an asynchronous multi-class traffic management (AMTM) scheme. We first establish an asynchronous TE paradigm in which distributed nodes locally perform low-complexity and low-delay traffic control based on link prices, and the TE server updates link prices to eliminate decision conflicts between edge nodes. By modeling the asynchronous TE paradigm as a control system with non-negligible control loop delay, we find that the traditional pricing strategy cannot simultaneously achieve a low packet loss rate and a low flow delivery delay. To address this issue, we propose a new pricing strategy based on the observations of virtual queues in intermediate nodes. We also present a system design and related algorithms that utilize a dynamic step size mechanism of link price update. Simulation results show that AMTM can effectively reduce the end-to-end flow delivery delay.

cs.NI↗

arXiv2016

Approximate Message Passing with Nearest Neighbor Sparsity Pattern Learning

We consider the problem of recovering clustered sparse signals with no prior knowledge of the sparsity pattern. Beyond simple sparsity, signals of interest often exhibits an underlying sparsity pattern which, if leveraged, can improve the reconstruction performance. However, the sparsity pattern is usually unknown a priori. Inspired by the idea of k-nearest neighbor (k-NN) algorithm, we propose an efficient algorithm termed approximate message passing with nearest neighbor sparsity pattern learning (AMP-NNSPL), which learns the sparsity pattern adaptively. AMP-NNSPL specifies a flexible spike and slab prior on the unknown signal and, after each AMP iteration, sets the sparse ratios as the average of the nearest neighbor estimates via expectation maximization (EM). Experimental results on both synthetic and real data demonstrate the superiority of our proposed algorithm both in terms of reconstruction performance and computational complexity.

cs.IT↗

arXiv2015

Message-Passing Receiver for Joint Channel Estimation and Decoding in 3D Massive MIMO-OFDM Systems

In this paper, we address the message-passing receiver design for the 3D massive MIMO-OFDM systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. Specially, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response (CIR) between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization (EM)-based method is derived to learn the channel power-delay-profile (PDP). The proposed joint algorithm is assessed in 3D massive MIMO systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and complexity.

cs.IT↗

arXiv2015

Concise Derivation of Complex Bayesian Approximate Message Passing via Expectation Propagation

In this paper, we address the problem of recovering complex-valued signals from a set of complex-valued linear measurements. Approximate message passing (AMP) is one state-of-the-art algorithm to recover real-valued sparse signals. However, the extension of AMP to complex-valued case is nontrivial and no detailed and rigorous derivation has been explicitly presented. To fill this gap, we extend AMP to complex Bayesian approximate message passing (CB-AMP) using expectation propagation (EP). This novel perspective leads to a concise derivation of CB-AMP without sophisticated transformations between the complex domain and the real domain. In addition, we have derived state evolution equations to predict the reconstruction performance of CB-AMP. Simulation results are presented to demonstrate the efficiency of CB-AMP and state evolution.

cs.IT↗