arXiv subjects

Dong In Kim

Publications and source records attributed to Dong In Kim.

At least 91 records · Page 5Linked to original sources

arXiv2025

Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments.

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arXiv2025

Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances.

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arXiv2025

Online Collaborative Resource Allocation and Task Offloading for Multi-access Edge Computing

Multi-access edge computing (MEC) is emerging as a promising paradigm to provide flexible computing services close to user devices (UDs). However, meeting the computation-hungry and delay-sensitive demands of UDs faces several challenges, including the resource constraints of MEC servers, inherent dynamic and complex features in the MEC system, and difficulty in dealing with the time-coupled and decision-coupled optimization. In this work, we first present an edge-cloud collaborative MEC architecture, where the MEC servers and cloud collaboratively provide offloading services for UDs. Moreover, we formulate an energy-efficient and delay-aware optimization problem (EEDAOP) to minimize the energy consumption of UDs under the constraints of task deadlines and long-term queuing delays. Since the problem is proved to be non-convex mixed integer nonlinear programming (MINLP), we propose an online joint communication resource allocation and task offloading approach (OJCTA). Specifically, we transform EEDAOP into a real-time optimization problem by employing the Lyapunov optimization framework. Then, to solve the real-time optimization problem, we propose a communication resource allocation and task offloading optimization method by employing the Tammer decomposition mechanism, convex optimization method, bilateral matching mechanism, and dependent rounding method. Simulation results demonstrate that the proposed OJCTA can achieve superior system performance compared to the benchmark approaches.

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arXiv2025

Embodied AI-Enhanced Vehicular Networks: An Integrated Large Language Models and Reinforcement Learning Method

This paper investigates adaptive transmission strategies in embodied AI-enhanced vehicular networks by integrating large language models (LLMs) for semantic information extraction and deep reinforcement learning (DRL) for decision-making. The proposed framework aims to optimize both data transmission efficiency and decision accuracy by formulating an optimization problem that incorporates the Weber-Fechner law, serving as a metric for balancing bandwidth utilization and quality of experience (QoE). Specifically, we employ the large language and vision assistant (LLAVA) model to extract critical semantic information from raw image data captured by embodied AI agents (i.e., vehicles), reducing transmission data size by approximately more than 90\% while retaining essential content for vehicular communication and decision-making. In the dynamic vehicular environment, we employ a generalized advantage estimation-based proximal policy optimization (GAE-PPO) method to stabilize decision-making under uncertainty. Simulation results show that attention maps from LLAVA highlight the model's focus on relevant image regions, enhancing semantic representation accuracy. Additionally, our proposed transmission strategy improves QoE by up to 36\% compared to DDPG and accelerates convergence by reducing required steps by up to 47\% compared to pure PPO. Further analysis indicates that adapting semantic symbol length provides an effective trade-off between transmission quality and bandwidth, achieving up to a 61.4\% improvement in QoE when scaling from 4 to 8 vehicles.

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arXiv2024

Hallucination-aware Optimization for Large Language Model-empowered Communications

Large Language Models (LLMs) have significantly advanced communications fields, such as Telecom Q\&A, mathematical modeling, and coding. However, LLMs encounter an inherent issue known as hallucination, i.e., generating fact-conflicting or irrelevant content. This problem critically undermines the applicability of LLMs in communication systems yet has not been systematically explored. Hence, this paper provides a comprehensive review of LLM applications in communications, with a particular emphasis on hallucination mitigation. Specifically, we analyze hallucination causes and summarize hallucination mitigation strategies from both model- and system-based perspectives. Afterward, we review representative LLM-empowered communication schemes, detailing potential hallucination scenarios and comparing the mitigation strategies they adopted. Finally, we present a case study of a Telecom-oriented LLM that utilizes a novel hybrid approach to enhance the hallucination-aware service experience. On the model side, we publish a Telecom hallucination dataset and apply direct preference optimization to fine-tune LLMs, resulting in a 20.6\% correct rate improvement. Moreover, we construct a mobile-edge mixture-of-experts architecture for optimal LLM expert activation. Our research aims to propel the field of LLM-empowered communications forward by detecting and minimizing hallucination impacts.

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arXiv2024

Large Language Model Based Multi-Objective Optimization for Integrated Sensing and Communications in UAV Networks

This letter investigates an unmanned aerial vehicle (UAV) network with integrated sensing and communication (ISAC) systems, where multiple UAVs simultaneously sense the locations of ground users and provide communication services with radars. To find the trade-off between communication and sensing (C\&S) in the system, we formulate a multi-objective optimization problem (MOP) to maximize the total network utility and the localization Cramér-Rao bounds (CRB) of ground users, which jointly optimizes the deployment and power control of UAVs. Inspired by the huge potential of large language models (LLM) for prediction and inference, we propose an LLM-enabled decomposition-based multi-objective evolutionary algorithm (LEDMA) for solving the highly non-convex MOP. We first adopt a decomposition-based scheme to decompose the MOP into a series of optimization sub-problems. We second integrate LLMs as black-box search operators with MOP-specifically designed prompt engineering into the framework of MOEA to solve optimization sub-problems simultaneously. Numerical results demonstrate that the proposed LEDMA can find the clear trade-off between C\&S and outperforms baseline MOEAs in terms of obtained Pareto fronts and convergence.

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arXiv2024

Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO approach

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. {In these networks,} an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem. In the formulated problem, beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices are jointly optimized subject to constraints on total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. {The approach leverages} dual sample strategies and generalized advantage estimation (GAE). In addition, to further simplify the design, we adopt the maximum ratio transmission (MRT) and zero-forcing (ZF) as beamforming vectors in the action space. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches in terms of achievable SEE and significantly improves convergence speed compared to the original PPO approach. Moreover, implementing the RSMA scheme and IRS contributes to overall system performance, {achieving approximately $19.67\%$ improvement over traditional multiple access schemes and $25.74\%$ improvement over networks without IRS deployment.

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arXiv2024

Adversarial Attacks Against Double RIS-Assisted MIMO Systems-based Autoencoder in Finite-Scattering Environments

Autoencoder permits the end-to-end optimization and design of wireless communication systems to be more beneficial than traditional signal processing. However, this emerging learning-based framework has weaknesses, especially sensitivity to physical attacks. This paper explores adversarial attacks against a double reconfigurable intelligent surface (RIS)-assisted multiple-input and multiple-output (MIMO)-based autoencoder, where an adversary employs encoded and decoded datasets to create adversarial perturbation and fool the system. Because of the complex and dynamic data structures, adversarial attacks are not unique, each having its own benefits. We, therefore, propose three algorithms generating adversarial examples and perturbations to attack the RIS-MIMO-based autoencoder, exploiting the gradient descent and allowing for flexibility via varying the input dimensions. Numerical results show that the proposed adversarial attack-based algorithm significantly degrades the system performance regarding the symbol error rate compared to the jamming attacks.

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arXiv2024

Generative AI in Data Center Networking: Fundamentals, Perspectives, and Case Study

Generative AI (GenAI), exemplified by Large Language Models (LLMs) such as OpenAI's ChatGPT, is revolutionizing various fields. Central to this transformation is Data Center Networking (DCN), which not only provides the computational power necessary for GenAI training and inference but also delivers GenAI-driven services to users. This article examines an interplay between GenAI and DCNs, highlighting their symbiotic relationship and mutual advancements. We begin by reviewing current challenges within DCNs and discuss how GenAI contributes to enhancing DCN capabilities through innovations, such as data augmentation, process automation, and domain transfer. We then focus on analyzing the distinctive characteristics of GenAI workloads on DCNs, gaining insights that catalyze the evolution of DCNs to more effectively support GenAI and LLMs. Moreover, to illustrate the seamless integration of GenAI with DCNs, we present a case study on full-lifecycle DCN digital twins. In this study, we employ LLMs equipped with Retrieval Augmented Generation (RAG) to formulate optimization problems for DCNs and adopt Diffusion-Deep Reinforcement Learning (DRL) for optimizing the RAG knowledge placement strategy. This approach not only demonstrates the application of advanced GenAI methods within DCNs but also positions the digital twin as a pivotal GenAI service operating on DCNs. We anticipate that this article can promote further research into enhancing the virtuous interaction between GenAI and DCNs.

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arXiv2024

Guiding IoT-Based Healthcare Alert Systems with Large Language Models

Healthcare alert systems (HAS) are undergoing rapid evolution, propelled by advancements in artificial intelligence (AI), Internet of Things (IoT) technologies, and increasing health consciousness. Despite significant progress, a fundamental challenge remains: balancing the accuracy of personalized health alerts with stringent privacy protection in HAS environments constrained by resources. To address this issue, we introduce a uniform framework, LLM-HAS, which incorporates Large Language Models (LLM) into HAS to significantly boost the accuracy, ensure user privacy, and enhance personalized health service, while also improving the subjective quality of experience (QoE) for users. Our innovative framework leverages a Mixture of Experts (MoE) approach, augmented with LLM, to analyze users' personalized preferences and potential health risks from additional textual job descriptions. This analysis guides the selection of specialized Deep Reinforcement Learning (DDPG) experts, tasked with making precise health alerts. Moreover, LLM-HAS can process Conversational User Feedback, which not only allows fine-tuning of DDPG but also deepen user engagement, thereby enhancing both the accuracy and personalization of health management strategies. Simulation results validate the effectiveness of the LLM-HAS framework, highlighting its potential as a groundbreaking approach for employing generative AI (GAI) to provide highly accurate and reliable alerts.

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arXiv2024

Generative AI based Secure Wireless Sensing for ISAC Networks

Integrated sensing and communications (ISAC) is expected to be a key technology for 6G, and channel state information (CSI) based sensing is a key component of ISAC. However, current research on ISAC focuses mainly on improving sensing performance, overlooking security issues, particularly the unauthorized sensing of users. In this paper, we propose a secure sensing system (DFSS) based on two distinct diffusion models. Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, guiding the ISAC system to appropriately activate wireless links and nodes, which ensures the sensing performance while minimizing the operation cost. Using the activated links and nodes, DFSS then employs the continuous conditional diffusion model to generate safeguarding signals, which are next modulated onto the pilot at the transmitter to mask fluctuations caused by user activities. As such, only ISAC devices authorized with the safeguarding signals can extract the true CSI for sensing, while unauthorized devices are unable to achieve the same sensing. Experiment results demonstrate that DFSS can reduce the activity recognition accuracy of the unauthorized devices by approximately 70%, effectively shield the user from the unauthorized surveillance.

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arXiv2024

Generative AI for Space-Air-Ground Integrated Networks

Recently, generative AI technologies have emerged as a significant advancement in artificial intelligence field, renowned for their language and image generation capabilities. Meantime, space-air-ground integrated network (SAGIN) is an integral part of future B5G/6G for achieving ubiquitous connectivity. Inspired by this, this article explores an integration of generative AI in SAGIN, focusing on potential applications and case study. We first provide a comprehensive review of SAGIN and generative AI models, highlighting their capabilities and opportunities of their integration. Benefiting from generative AI's ability to generate useful data and facilitate advanced decision-making processes, it can be applied to various scenarios of SAGIN. Accordingly, we present a concise survey on their integration, including channel modeling and channel state information (CSI) estimation, joint air-space-ground resource allocation, intelligent network deployment, semantic communications, image extraction and processing, security and privacy enhancement. Next, we propose a framework that utilizes a Generative Diffusion Model (GDM) to construct channel information map to enhance quality of service for SAGIN. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss potential research directions for generative AI-enabled SAGIN.

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arXiv2024

Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission

In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by the complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregates them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated.

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arXiv2024

Hierarchical Micro-Segmentations for Zero-Trust Services via Large Language Model (LLM)-enhanced Graph Diffusion

In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize policy boosting and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%.

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arXiv2024

Overlay Space-Air-Ground Integrated Networks with SWIPT-Empowered Aerial Communications

In this article, we consider overlay space-air-ground integrated networks (OSAGINs) where a low earth orbit (LEO) satellite communicates with ground users (GUs) with the assistance of an energy-constrained coexisting air-to-air (A2A) network. Particularly, a non-linear energy harvester with a hybrid SWIPT utilizing both power-splitting and time-switching energy harvesting (EH) techniques is employed at the aerial transmitter. Specifically, we take the random locations of the satellite, ground and aerial receivers to investigate the outage performance of both the satellite-to-ground and aerial networks leveraging the stochastic tools. By taking into account the Shadowed-Rician fading for satellite link, the Nakagami-\emph{m} for ground link, and the Rician fading for aerial link, we derive analytical expressions for the outage probability of these networks. For a comprehensive analysis of aerial network, we consider both the perfect and imperfect successive interference cancellation (SIC) scenarios. Through our analysis, we illustrate that, unlike linear EH, the implementation of non-linear EH provides accurate figures for any target rate, underscoring the significance of using non-linear EH models. Additionally, the influence of key parameters is emphasized, providing guidelines for the practical design of an energy-efficient as well as spectrum-efficient future non-terrestrial networks. Monte Carlo simulations validate the accuracy of our theoretical developments.

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arXiv2024

Safeguarding Next Generation Multiple Access Using Physical Layer Security Techniques: A Tutorial

Driven by the ever-increasing requirements of ultra-high spectral efficiency, ultra-low latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next generation multiple access schemes to facilitate provisioning of these stringent demands. This inspires the embrace of non-orthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats, due to the open nature of the air interface, the broadcast characteristic of radio propagation as well as intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security aware design, for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview on the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends.

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arXiv2024

Optimizing Generative AI Networking: A Dual Perspective with Multi-Agent Systems and Mixture of Experts

In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixture of experts (MoE) frameworks is becoming increasingly important. Motivated by this, this article studies the contrasting and converging of MAS and MoE in AIGC-enabled networking. First, we discuss the architectural designs, operational procedures, and inherent advantages of using MAS and MoE in generative AI to explore its functionality and applications fully. Next, we review the applications of MAS and MoE frameworks in content generation and resource allocation, emphasizing their impact on networking operations. Subsequently, we propose a novel multi-agent-enabled MoE-proximal policy optimization (MoE-PPO) framework for 3D object generation and data transfer scenarios. The framework uses MAS for dynamic task coordination of each network service provider agent and MoE for expert-driven execution of respective tasks, thereby improving overall system efficiency and adaptability. The simulation results demonstrate the effectiveness of our proposed framework and significantly improve the performance indicators under different network conditions. Finally, we outline potential future research directions.

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arXiv2024

Wireless Information and Energy Transfer in the Era of 6G Communications

Wireless information and energy transfer (WIET) represents an emerging paradigm which employs controllable transmission of radio-frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned 6G use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such as smart cities and smart grids. Meeting the quality-of-service demands of WIET, in terms of both data transfer and power delivery, requires effective co-design of the information and energy signals. In this article, we present the main principles and design aspects of WIET, focusing on its integration in 6G networks. First, we discuss how conventional communication notions such as resource allocation and waveform design need to be revisited in the context of WIET. Next, we consider various candidate 6G technologies that can boost WIET efficiency, namely, holographic multiple-input multiple-output, near-field beamforming, terahertz communication, intelligent reflecting surfaces (IRSs), and reconfigurable (fluid) antenna arrays. We introduce respective WIET design methods, analyze the promising performance gains of these WIET systems, and discuss challenges, open issues, and future research directions. Finally, a near-field energy beamforming scheme and a power-based IRS beamforming algorithm are experimentally validated using a wireless energy transfer testbed. The vision of WIET in communication systems has been gaining momentum in recent years, with constant progress with respect to theoretical but also practical aspects. The comprehensive overview of the state of the art of WIET presented in this paper highlights the potentials of WIET systems as well as their overall benefits in 6G networks.

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