arXiv subjects

Ruichen Zhang

Publications and source records attributed to Ruichen Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Enabling Training-Free Semantic Communication Systems with Generative Diffusion Models

Semantic communication (SemCom) has recently emerged as a promising paradigm for next-generation wireless systems. Empowered by advanced artificial intelligence (AI) technologies, SemCom has achieved significant improvements in transmission quality and efficiency. However, existing SemCom systems either rely on training over large datasets and specific channel conditions or suffer from performance degradation under channel noise when operating in a training-free manner. To address these issues, we explore the use of generative diffusion models (GDMs) as training-free SemCom systems. Specifically, we design a semantic encoding and decoding method based on the inversion and sampling process of the denoising diffusion implicit model (DDIM), which introduces a two-stage forward diffusion process, split between the transmitter and receiver to enhance robustness against channel noise. Moreover, we optimize sampling steps to compensate for the increased noise level caused by channel noise. We also conduct a brief analysis to provide insights about this design. Simulations on the Kodak dataset validate that the proposed system outperforms the existing baseline SemCom systems across various metrics.

cs.IT↗

arXiv2026

Incentive Mechanism Design for Resource Management in Satellite Networks: A Comprehensive Survey

Resource management is one of the challenges in satellite networks due to their high mobility, wide coverage, long propagation distances, and stringent constraints on energy, communication, and computation resources. Traditional resource allocation approaches rely only on hard and rigid system performance metrics. Meanwhile, incentive mechanisms, which are based on game theory and auction theory, investigate systems from the "economic" perspective in addition to the "system" perspective. Particularly, incentive mechanisms are able to take into account rationality and other behavior of human users into account, which guarantees benefits/utility of all system entities, thereby improving the scalability, adaptability, and fairness in resource allocation. This paper presents a comprehensive survey of incentive mechanism design for resource management in satellite networks. The paper covers key issues in the satellite networks, such as communication resource allocation, computation offloading, privacy and security, and coordination. We conclude with future research directions including learning-based mechanism design for satellite networks.

cs.NI↗

arXiv2026

Rethinking Secure Semantic Communications in the Age of Generative and Agentic AI: Threats and Opportunities

Semantic communication (SemCom) improves communication efficiency by transmitting task-relevant information instead of raw bits and is expected to be a key technology for 6G networks. Recent advances in generative AI (GenAI) further enhance SemCom by enabling robust semantic encoding and decoding under limited channel conditions. However, these efficiency gains also introduce new security and privacy vulnerabilities. Due to the broadcast nature of wireless channels, eavesdroppers can also use powerful GenAI-based semantic decoders to recover private information from intercepted signals. Moreover, rapid advances in agentic AI enable eavesdroppers to perform long-term and adaptive inference through the integration of memory, external knowledge, and reasoning capabilities. This allows eavesdroppers to further infer user private behavior and intent beyond the transmitted content. Motivated by these emerging challenges, this paper comprehensively rethinks the security and privacy of SemCom systems in the age of generative and agentic AI. We first present a systematic taxonomy of eavesdropping threat models in SemCom systems. Then, we provide insights into how GenAI and agentic AI can enhance eavesdropping threats. Meanwhile, we also highlight potential opportunities for leveraging GenAI and agentic AI to design privacy-preserving SemCom systems.

eess.SP↗

arXiv2025

Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles

The energy optimization and demand side management (DSM) of Internet of Things (IoT)-enabled microgrids are being transformed by generative artificial intelligence, such as large language models (LLMs). This paper explores the integration of LLMs into energy management, and emphasizes their roles in automating the optimization of DSM strategies with Internet of Electric Vehicles (IoEV) as a representative example of the Internet of Vehicles (IoV). We investigate challenges and solutions associated with DSM and explore the new opportunities presented by leveraging LLMs. Then, we propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. The results demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles, and highlight our solution's significant advancements in energy efficiency and user adaptability. This work shows LLMs' potential in energy optimization of the IoT-enabled microgrids and promotes intelligent DSM solutions.

cs.LG↗

arXiv2025

LLM-Empowered Agentic AI for QoE-Aware Network Slicing Management in Industrial IoT

The Industrial Internet of Things (IIoT) requires networks that deliver ultra-low latency, high reliability, and cost efficiency, which traditional optimization methods and deep reinforcement learning (DRL)-based approaches struggle to provide under dynamic and heterogeneous workloads. To address this gap, large language model (LLM)-empowered agentic AI has emerged as a promising paradigm, integrating reasoning, planning, and adaptation to enable QoE-aware network management. In this paper, we explore the integration of agentic AI into QoE-aware network slicing for IIoT. We first review the network slicing management architecture, QoE metrics for IIoT applications, and the challenges of dynamically managing heterogeneous network slices, while highlighting the motivations and advantages of adopting agentic AI. We then present the workflow of agentic AI-based slicing management, illustrating the full lifecycle of AI agents from processing slice requests to constructing slice instances and performing dynamic adjustments. Furthermore, we propose an LLM-empowered agentic AI approach for slicing management, which integrates a retrieval-augmented generation (RAG) module for semantic intent inference, a DRL-based orchestrator for slicing configuration, and an incremental memory mechanism for continual learning and adaptation. Through a case study on heterogeneous slice management, we demonstrate that the proposed approach significantly outperforms other baselines in balancing latency, reliability, and cost, and achieves up to a 19% improvement in slice availability ratio.

cs.NI↗

arXiv2025

Tutorial on Large Language Model-Enhanced Reinforcement Learning for Wireless Networks

Reinforcement Learning (RL) has shown remarkable success in enabling adaptive and data-driven optimization for various applications in wireless networks. However, classical RL suffers from limitations in generalization, learning feedback, interpretability, and sample efficiency in dynamic wireless environments. Large Language Models (LLMs) have emerged as a transformative Artificial Intelligence (AI) paradigm with exceptional capabilities in knowledge generalization, contextual reasoning, and interactive generation, which have demonstrated strong potential to enhance classical RL. This paper serves as a comprehensive tutorial on LLM-enhanced RL for wireless networks. We propose a taxonomy to categorize the roles of LLMs into four critical functions: state perceiver, reward designer, decision-maker, and generator. Then, we review existing studies exploring how each role of LLMs enhances different stages of the RL pipeline. Moreover, we provide a series of case studies to illustrate how to design and apply LLM-enhanced RL in low-altitude economy networking, vehicular networks, and space-air-ground integrated networks. Finally, we conclude with a discussion on potential future directions for LLM-enhanced RL and offer insights into its future development in wireless networks.

cs.NI↗

arXiv2025

Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness

The rapid growth of the low-altitude economy has driven the widespread adoption of unmanned aerial vehicles (UAVs). This growing deployment presents new challenges for UAV trajectory planning in complex urban environments. However, existing studies often overlook key factors, such as urban airspace constraints and economic efficiency, which are essential in low-altitude economy contexts. Deep reinforcement learning (DRL) is regarded as a promising solution to these issues, while its practical adoption remains limited by low learning efficiency. To overcome this limitation, we propose a novel UAV trajectory planning framework that combines DRL with large language model (LLM) reasoning to enable safe, compliant, and economically viable path planning. Experimental results demonstrate that our method significantly outperforms existing baselines across multiple metrics, including data collection rate, collision avoidance, successful landing, regulatory compliance, and energy efficiency. These results validate the effectiveness of our approach in addressing UAV trajectory planning key challenges under constraints of the low-altitude economy networking.

cs.AI↗

arXiv2025

Towards Edge General Intelligence: Knowledge Distillation for Mobile Agentic AI

Edge General Intelligence (EGI) represents a paradigm shift in mobile edge computing, where intelligent agents operate autonomously in dynamic, resource-constrained environments. However, the deployment of advanced agentic AI models on mobile and edge devices faces significant challenges due to limited computation, energy, and storage resources. To address these constraints, this survey investigates the integration of Knowledge Distillation (KD) into EGI, positioning KD as a key enabler for efficient, communication-aware, and scalable intelligence at the wireless edge. In particular, we emphasize KD techniques specifically designed for wireless communication and mobile networking, such as channel-aware self-distillation, cross-model Channel State Information (CSI) feedback distillation, and robust modulation/classification distillation. Furthermore, we review novel architectures natively suited for KD and edge deployment, such as Mamba, RWKV (Receptance, Weight, Key, Value) and Cross-Architecture distillation, which enhance generalization capabilities. Subsequently, we examine diverse applications in which KD-driven architectures enable EGI across vision, speech, and multimodal tasks. Finally, we highlight the key challenges and future directions for KD in EGI. This survey aims to provide a comprehensive reference for researchers exploring KD-driven frameworks for mobile agentic AI in the era of EGI.

cs.IT↗

arXiv2025

Toward Integrated Air-Ground Computing and Communications: A Synergy of Computing Power Networks and Low-Altitude Economy Network

With the rapid rise of the Low-Altitude Economy (LAE), the demand for intelligent processing and real-time response in services such as aerial traffic, emergency communications, and environmental monitoring continues to grow. Meanwhile, the Computing Power Network (CPN) aims to integrate global computing resources and perform on-demand scheduling to efficiently handle services from diverse sources. However, it is limited by static deployment and limited adaptability. In this paper, we analyze the complementary relationship between LAE and CPN and propose a novel air-ground collaborative intelligent service provision with an agentification paradigm. Through synergy between LAE and CPNs, computing and communication services are jointly scheduled and collaboratively optimized to enhance the execution efficiency of low-altitude services and improve the flexibility of CPNs. It also integrates LAE's strengths in aerial sensing, mobile coverage, and dynamic communication links, forming a cloud-edge-air collaborative framework. Hence, we review the characteristics and limitations of both LAE and CPN and explore how they can cooperate to overcome these limitations. Then we demonstrate the flexibility of the integrated CPN and LAE framework through a case study. Finally, we summarize the key challenges in constructing an integrated air-ground computing and communication system and discuss future research directions toward emerging technologies.

cs.NI↗

arXiv2025

Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions

Next-generation Multiple-Input Multiple-Output (MIMO) is expected to be intelligent and scalable. In this paper, we study Large Language Model (LLM)-enabled next-generation MIMO networks. Firstly, we provide an overview of the development, fundamentals, and challenges of the next-generation MIMO. Then, we propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents with the aid of LLM and Retrieval Augmented Generation (RAG). Next, we comprehensively discuss the features and advantages of the generative AI agent framework. More importantly, to tackle existing challenges of next-generation MIMO, we discuss generative AI agent-enabled next-generation MIMO networks from the perspective of performance analysis, signal processing, and resource allocation. Furthermore, we present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis in complex configuration scenarios. These examples highlight how the integration of generative AI agents can significantly enhance the analysis and design of next-generation MIMO systems. Finally, we discuss important potential research future directions.

cs.NI↗

arXiv2025

Wireless Laser Power Transfer for Low-altitude Uncrewed Aerial Vehicle-assisted Internet of Things: Paradigms, Challenges, and Solutions

Low-altitude uncrewed aerial vehicles (UAVs) have become integral enablers for the Internet of Things (IoT) by offering enhanced coverage, improved connectivity and access to remote areas. A critical challenge limiting their operational capacity lies in the energy constraints of both aerial platforms and ground-based sensors. This paper explores WLPT as a transformative solution for sustainable energy provisioning in UAV-assisted IoT networks. We first systematically investigate the fundamental principles of WLPT and analysis the comparative advantages. Then, we introduce three operational paradigms for system integration, identify key challenges, and discuss corresponding potential solutions. In case study, we propose a multi-agent reinforcement learning framework to address the coordination and optimization challenges in WLPT-enabled UAV-assisted IoT data collection. Simulation results demonstrate that our framework significantly improves energy sustainability and data freshness. Finally, we discuss some future directions.

cs.NI↗

arXiv2025

SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks

Deep joint source-channel coding (JSCC) has emerged as a promising paradigm for semantic communication, delivering significant performance gains over conventional separate coding schemes. However, existing JSCC frameworks remain vulnerable to physical-layer adversarial threats, such as pilot spoofing and subcarrier jamming, compromising semantic fidelity. In this paper, we propose SecDiff, a plug-and-play, diffusion-aided decoding framework that significantly enhances the security and robustness of deep JSCC under adversarial wireless environments. Different from prior diffusion-guided JSCC methods that suffer from high inference latency, SecDiff employs pseudoinverse-guided sampling and adaptive guidance weighting, enabling flexible step-size control and efficient semantic reconstruction. To counter jamming attacks, we introduce a power-based subcarrier masking strategy and recast recovery as a masked inpainting problem, solved via diffusion guidance. For pilot spoofing, we formulate channel estimation as a blind inverse problem and develop an expectation-minimization (EM)-driven reconstruction algorithm, guided jointly by reconstruction loss and a channel operator. Notably, our method alternates between pilot recovery and channel estimation, enabling joint refinement of both variables throughout the diffusion process. Extensive experiments over orthogonal frequency-division multiplexing (OFDM) channels under adversarial conditions show that SecDiff outperforms existing secure and generative JSCC baselines by achieving a favorable trade-off between reconstruction quality and computational cost. This balance makes SecDiff a promising step toward practical, low-latency, and attack-resilient semantic communications.

cs.CV↗

arXiv2025

EditCast3D: Single-Frame-Guided 3D Editing with Video Propagation and View Selection

Recent advances in foundation models have driven remarkable progress in image editing, yet their extension to 3D editing remains underexplored. A natural approach is to replace the image editing modules in existing workflows with foundation models. However, their heavy computational demands and the restrictions and costs of closed-source APIs make plugging these models into existing iterative editing strategies impractical. To address this limitation, we propose EditCast3D, a pipeline that employs video generation foundation models to propagate edits from a single first frame across the entire dataset prior to reconstruction. While editing propagation enables dataset-level editing via video models, its consistency remains suboptimal for 3D reconstruction, where multi-view alignment is essential. To overcome this, EditCast3D introduces a view selection strategy that explicitly identifies consistent and reconstruction-friendly views and adopts feedforward reconstruction without requiring costly refinement. In combination, the pipeline both minimizes reliance on expensive image editing and mitigates prompt ambiguities that arise when applying foundation models independently across images. We evaluate EditCast3D on commonly used 3D editing datasets and compare it against state-of-the-art 3D editing baselines, demonstrating superior editing quality and high efficiency. These results establish EditCast3D as a scalable and general paradigm for integrating foundation models into 3D editing pipelines. The code is available at https://github.com/UNITES-Lab/EditCast3D

cs.CV↗

arXiv2025

From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions

Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems by enabling autonomous agents to continually adapt and improve without human intervention. Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and workflows in response to environmental dynamics. This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques, including tool intelligence, workflow optimization, self-reflection, and evolutionary learning. We further propose a multi-agent cooperative self-evolving agentic AI framework, where multiple large language models (LLMs) are assigned role-specialized prompts under the coordination of a supervisor agent. Through structured dialogue, iterative feedback, and systematic validation, the system autonomously executes the entire life cycle without human intervention. A case study on antenna evolution in low-altitude wireless networks (LAWNs) demonstrates how the framework autonomously upgrades fixed antenna optimization into movable antenna optimization. Experimental results show that the proposed self-evolving agentic AI autonomously improves beam gain and restores degraded performance by up to 52.02%, consistently surpassing the fixed baseline with little to no human intervention and validating its adaptability and robustness for next-generation wireless intelligence.

cs.AI↗

arXiv2025

A Synergy of Computing Power Networks and Low-Altitude Economy Intelligent Communications: Challenges, Design Principles, and Research Directions

The rapid development of the Low-Altitude Economy (LAE) has created opportunities for emerging services such as autonomous aerial transportation, aerial sensing, and emergency response, all of which rely on efficient and intelligent communications. However, LAE intelligent communications face several challenges, including the limited computational capacity of aerial nodes, the lack of cross-scenario generalization, and the complexity of heterogeneous demands. Meanwhile, Computing Power Networks (CPNs) have emerged as a new paradigm for integrating distributed computing, networking, and storage resources, but they are also constrained by static deployment and limited adaptability. In this survey, we explore the synergy between LAE intelligent communications and CPNs. We first analyze how CPNs can support LAE intelligent communications in areas such as air-ground collaborative control, AI training, communication-computation co-ptimization, and ubiquitous low-altitude information processing. Conversely, we discuss how LAE intelligent communications can enhance CPNs through mobility-assisted control, distributed intelligent training, dynamic routing, and in-network aerial computing. Finally, based on these insights, we outline design principles and future research directions for integrated CPN-LAE systems. This work provides a comprehensive foundation for building flexible, adaptive, and resilient architectures that leverage the synergy between CPNs and LAE to deliver high-quality and sustainable low-altitude services.

cs.NI↗

arXiv2025

AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives

Artificial Intelligence (AI) techniques play a pivotal role in optimizing wireless communication networks. However, traditional deep learning approaches often act as closed boxes, lacking the structured reasoning abilities needed to tackle complex, multi-step decision problems. This survey provides a comprehensive review and outlook of reasoning-enabled AI in wireless communication networks, with a focus on Large Language Models (LLMs) and other advanced reasoning paradigms. In particular, LLM-based agents can combine reasoning with long-term planning, memory, tool utilization, and autonomous cross-layer control to dynamically optimize network operations with minimal human intervention. We begin by outlining the evolution of intelligent wireless networking and the limitations of conventional AI methods. We then introduce emerging AI reasoning techniques. Furthermore, we establish a classification system applicable to wireless network tasks. We also present a layer-by-layer examination for AI reasoning, covering the physical, data link, network, transport, and application layers. For each part, we identify key challenges and illustrate how AI reasoning methods can improve AI-based wireless communication performance. Finally, we discuss key research directions for AI reasoning toward future wireless communication networks. By combining insights from both communications and AI, this survey aims to chart a path for integrating reasoning techniques into the next-generation wireless networks.

cs.NI↗

arXiv2025

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial

The rapid advancement toward sixth-generation (6G) wireless networks has significantly intensified the complexity and scale of optimization problems, including resource allocation and trajectory design, often formulated as combinatorial problems in large discrete decision spaces. However, traditional optimization methods, such as heuristics and deep reinforcement learning (DRL), struggle to meet the demanding requirements of real-time adaptability, scalability, and dynamic handling of user intents in increasingly heterogeneous and resource-constrained network environments. Large language models (LLMs) present a transformative paradigm by enabling natural language-driven problem formulation, context-aware reasoning, and adaptive solution refinement through advanced semantic understanding and structured reasoning capabilities. This paper provides a systematic and comprehensive survey of LLM-enabled optimization frameworks tailored for wireless networks. We first introduce foundational design concepts and distinguish LLM-enabled methods from conventional optimization paradigms. Subsequently, we critically analyze key enabling methodologies, including natural language modeling, solver collaboration, and solution verification processes. Moreover, we explore representative case studies to demonstrate LLMs' transformative potential in practical scenarios such as optimization formulation, low-altitude economy networking, and intent networking. Finally, we discuss current research challenges, examine prominent open-source frameworks and datasets, and identify promising future directions to facilitate robust, scalable, and trustworthy LLM-enabled optimization solutions for next-generation wireless networks.

cs.NI↗

arXiv2025

Towards Reliable Service Provisioning for Dynamic UAV Clusters in Low-Altitude Economy Networks

Unmanned Aerial Vehicle (UAV) cluster services are crucial for promoting the low-altitude economy by enabling scalable, flexible, and adaptive aerial networks. To meet diverse service demands, clusters must dynamically incorporate a New UAVs (NUAVs) or an Existing UAV (EUAV). However, achieving sustained service reliability remains challenging due to the need for efficient and scalable NUAV authentication, privacy-preserving cross-cluster authentication for EUAVs, and robust protection of the cluster session key, including both forward and backward secrecy. To address these challenges, we propose a Lightweight and Privacy-Preserving Cluster Authentication and Session Key Update (LP2-CASKU) scheme tailored for dynamic UAV clusters in low-altitude economy networks. LP2-CASKU integrates an efficient batch authentication mechanism that simultaneously authenticates multiple NUAVs with minimal communication overhead. It further introduces a lightweight cross-cluster authentication mechanism that ensures EUAV anonymity and unlinkability. Additionally, a secure session key update mechanism is incorporated to maintain key confidentiality over time, thereby preserving both forward and backward secrecy. We provide a comprehensive security analysis and evaluate LP2-CASKU performance through both theoretical analysis and OMNeT++ simulations. Experimental results demonstrate that, compared to the baseline, LP2-CASKU achieves a latency reduction of 82.8%-90.8% by across different UAV swarm configurations and network bitrates, demonstrating strong adaptability to dynamic communication environments. Besides, under varying UAV swarm configurations, LP2-CASKU reduces the energy consumption by approximately 37.6-72.6%, while effectively supporting privacy-preserving authentication in highly dynamic UAV cluster environments.

cs.CR↗