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Xuemin Shen

Publications and source records attributed to Xuemin Shen.

At least 73 records · Page 4Linked to original sources
arXiv2024

Interactive AI with Retrieval-Augmented Generation for Next Generation Networking

With the advance of artificial intelligence (AI), the emergence of Google Gemini and OpenAI Q* marks the direction towards artificial general intelligence (AGI). To implement AGI, the concept of interactive AI (IAI) has been introduced, which can interactively understand and respond not only to human user input but also to dynamic system and network conditions. In this article, we explore an integration and enhancement of IAI in networking. We first comprehensively review recent developments and future perspectives of AI and then introduce the technology and components of IAI. We then explore the integration of IAI into the next-generation networks, focusing on how implicit and explicit interactions can enhance network functionality, improve user experience, and promote efficient network management. Subsequently, we propose an IAI-enabled network management and optimization framework, which consists of environment, perception, action, and brain units. We also design the pluggable large language model (LLM) module and retrieval augmented generation (RAG) module to build the knowledge base and contextual memory for decision-making in the brain unit. We demonstrate the effectiveness of the framework through case studies. Finally, we discuss potential research directions for IAI-based networks.

cs.NI↗
arXiv2024

Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles

Cooperative perception (CP) is a key technology to facilitate consistent and accurate situational awareness for connected and autonomous vehicles (CAVs). To tackle the network resource inefficiency issue in traditional broadcast-based CP, unicast-based CP has been proposed to associate CAV pairs for cooperative perception via vehicle-to-vehicle transmission. In this paper, we investigate unicast-based CP among CAV pairs. With the consideration of dynamic perception workloads and channel conditions due to vehicle mobility and dynamic radio resource availability, we propose an adaptive cooperative perception scheme for CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and human-driven vehicles. We aim to determine when to switch between cooperative perception and stand-alone perception for each CAV pair, and allocate communication and computing resources to cooperative CAV pairs for maximizing the computing efficiency gain under perception task delay requirements. A model-assisted multi-agent reinforcement learning (MARL) solution is developed, which integrates MARL for an adaptive CAV cooperation decision and an optimization model for communication and computing resource allocation. Simulation results demonstrate the effectiveness of the proposed scheme in achieving high computing efficiency gain, as compared with benchmark schemes.

cs.NI↗
arXiv2023

User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality

In this paper, we present a novel content caching and delivery approach for mobile virtual reality (VR) video streaming. The proposed approach aims to maximize VR video streaming performance, i.e., minimizing video frame missing rate, by proactively caching popular VR video chunks and adaptively scheduling computing resources at an edge server based on user and network dynamics. First, we design a scalable content placement scheme for deciding which video chunks to cache at the edge server based on tradeoffs between computing and caching resource consumption. Second, we propose a machine learning-assisted VR video delivery scheme, which allocates computing resources at the edge server to satisfy video delivery requests from multiple VR headsets. A Whittle index-based method is adopted to reduce the video frame missing rate by identifying network and user dynamics with low signaling overhead. Simulation results demonstrate that the proposed approach can significantly improve VR video streaming performance over conventional caching and computing resource scheduling strategies.

eess.SP↗
arXiv2023

Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study

As the next-generation paradigm for content creation, AI-Generated Content (AIGC), i.e., generating content automatically by Generative AI (GAI) based on user prompts, has gained great attention and success recently. With the ever-increasing power of GAI, especially the emergence of Pretrained Foundation Models (PFMs) that contain billions of parameters and prompt engineering methods (i.e., finding the best prompts for the given task), the application range of AIGC is rapidly expanding, covering various forms of information for human, systems, and networks, such as network designs, channel coding, and optimization solutions. In this article, we present the concept of mobile-edge AI-Generated Everything (AIGX). Specifically, we first review the building blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX applications. Then, we present a unified mobile-edge AIGX framework, which employs edge devices to provide PFM-empowered AIGX services and optimizes such services via prompt engineering. More importantly, we demonstrate that suboptimal prompts lead to poor generation quality, which adversely affects user satisfaction, edge network performance, and resource utilization. Accordingly, we conduct a case study, showcasing how to train an effective prompt optimizer using ChatGPT and investigating how much improvement is possible with prompt engineering in terms of user experience, quality of generation, and network performance.

cs.NI↗
arXiv2023

The Age of Generative AI and AI-Generated Everything

Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents ``AI-Generated Everything'' (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks.

cs.NI↗
arXiv2023

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks.

cs.NI↗
arXiv2023

Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective

As generative artificial intelligence (GAI) models continue to evolve, their generative capabilities are increasingly enhanced and being used extensively in content generation. Beyond this, GAI also excels in data modeling and analysis, benefitting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems. Specifically, we first provide an overview of GAI and ISAC, touching on GAI's potential support across multiple layers of ISAC. We then concentrate on the physical layer, investigating GAI's applications from various perspectives thoroughly, such as channel estimation, and demonstrate the value of these GAI-enhanced physical layer technologies for ISAC systems. In the case study, the proposed diffusion model-based method effectively estimates the signal direction of arrival under the near-field condition based on the uniform linear array, when antenna spacing surpassing half the wavelength. With a mean square error of 1.03 degrees, it confirms GAI's support for the physical layer in near-field sensing and communications.

eess.SP↗
arXiv2023

Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices' resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a resource-aware data augmentation method that effectively mitigates the data heterogeneity while ensuring efficient FL training. We first quantify the relationship between the training data amount and the learning performance. We then study the FIMI optimization problem with the objective of minimizing the device-side overall energy consumption subject to required learning performance constraints. The decomposition-based analysis and the cross-entropy searching method are leveraged to derive the solution, where each device is assigned suitable AI-synthesized data and resource utilization policy. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy in comparison with the existing methods. Meanwhile, FIMI can significantly enhance the converged global accuracy under the non-independently-and-identically distribution (non-IID) data.

cs.LG↗
arXiv2023

Voltage-tunable giant nonvolatile multiple-state resistance in sliding-interlayer ferroelectric h-BN van der Waals multiferroic tunnel junction

Multiferroic tunnel junctions (MFTJs) based on two-dimensional (2D) van der Waals heterostructures with sharp and clean interfaces at the atomic scale are crucial for applications in nanoscale multi-resistive logic memory devices. The recently discovered sliding ferroelectricity in 2D van der Waals materials has opened new avenues for ferroelectric-based devices. Here, we theoretically investigate the spin-dependent electronic transport properties of Fe$_3$GeTe$_2$/graphene/bilayer-$h$-BN/graphene/CrI$_3$ (FGT/Gr-BBN-Gr/CrI) all-vdW MFTJs by employing the nonequilibrium Green's function combined with density functional theory. We demonstrate that such FGT/Gr-BBN-Gr/CrI MFTJs exhibit four non-volatile resistance states associated with different staking orders of sliding ferroelectric BBN and magnetization alignment of ferromagnetic free layer CrI$_3$, with a maximum tunnel magnetoresistance (electroresistance) ratio, i.e., TMR (TER) up to $\sim$$3.36\times10^{4}$\% ($\sim$$6.68\times10^{3}$\%) at a specific bias voltage. Furthermore, the perfect spin filtering and remarkable negative differential resistance effects are evident in our MFTJs. We further discover that the TMR, TER, and spin polarization ratio under an equilibrium state can be enhanced by the application of in-plane biaxial strain. This work shows that the giant tunneling resistance ratio, multiple resistance states, and excellent spin-polarized transport properties of sliding ferroelectric BBN-based MFTJs indicate its significant potential in nonvolatile memories.

cond-mat.mes-hall↗
arXiv2023

AI-Assisted Slicing-Based Resource Management for Two-Tier Radio Access Networks

While network slicing has become a prevalent approach to service differentiation, radio access network (RAN) slicing remains challenging due to the need of substantial adaptivity and flexibility to cope with the highly dynamic network environment in RANs. In this paper, we develop a slicing-based resource management framework for a two-tier RAN to support multiple services with different quality of service (QoS) requirements. The developed framework focuses on base station (BS) service coverage (SC) and interference management for multiple slices, each of which corresponds to a service. New designs are introduced in the spatial, temporal, and slice dimensions to cope with spatiotemporal variations in data traffic, balance adaptivity and overhead of resource management, and enhance flexibility in service differentiation. Based on the proposed framework, an energy efficiency maximization problem is formulated, and an artificial intelligence (AI)-assisted approach is proposed to solve the problem. Specifically, a deep unsupervised learning-assisted algorithm is proposed for searching the optimal SC of the BSs, and an optimization-based analytical solution is found for managing interference among BSs. Simulation results under different data traffic distributions demonstrate that our proposed slicing-based resource management framework, empowered by the AI-assisted approach, outperforms the benchmark frameworks and achieves a close-to-optimal performance in energy efficiency.

cs.NI↗
arXiv2023

Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming

Multicast short video streaming (MSVS) can effectively reduce network traffic load by delivering identical video sequences to multiple users simultaneously. The existing MSVS schemes mainly rely on the aggregated video requests to reserve bandwidth and computing resources, which cannot satisfy users' diverse and dynamic service requirements, particularly when users' swipe behaviors exhibit spatiotemporal fluctuation. In this paper, we propose a user-centric resource management scheme based on the digital twin (DT) technique, which aims to enhance user satisfaction as well as reduce resource consumption. Firstly, we design a user DT (UDT)-assisted resource reservation framework. Specifically, UDTs are constructed for individual users, which store users' historical data for updating multicast groups and abstracting useful information. The swipe probability distributions and recommended video lists are abstracted from UDTs to predict bandwidth and computing resource demands. Parameterized sigmoid functions are leveraged to characterize multicast groups' user satisfaction. Secondly, we formulate a joint non-convex bandwidth and computing resource reservation problem which is transformed into a convex piecewise problem by utilizing a tangent function to approximately substitute the concave part. A low-complexity scheduling algorithm is then developed to find the optimal resource reservation decisions. Simulation results based on the real-world dataset demonstrate that the proposed scheme outperforms benchmark schemes in terms of user satisfaction and resource consumption.

cs.NI↗
arXiv2023

Knowledge-Driven Resource Allocation for D2D Networks: A WMMSE Unrolled Graph Neural Network Approach

This paper proposes an novel knowledge-driven approach for resource allocation in device-to-device (D2D) networks using a graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the dynamic network environment, our proposed approach incorporates the deep unrolling of the weighted minimum mean square error (WMMSE) algorithm, referred to as domain knowledge, into GNN, thereby reducing computational delay and sample complexity while adapting to various data distributions. Specifically, the aggregation and update functions in the GNN architecture are designed by utilizing the summation and power calculation components of the WMMSE algorithm, which leads to improved model generalization and interpretabiliy. Theoretical analysis of the proposed approach reveals its capability to simplify intricate end-to-end mappings and diminish the model exploration space, resulting in increased network expressiveness and enhanced optimization performance. Simulation results demonstrate the robustness, scalability, and strong performance of the proposed knowledge-driven resource allocation approach across diverse communication topologies without retraining. Our findings contribute to the development of efficient and scalable wireless resource management solutions for distributed and dynamic networks with strict latency requirements.

eess.SY↗
arXiv2023

Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at \url{https://github.com/UNIC-Lab/LOGNN}.

eess.SY↗
arXiv2023

Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality

In this paper, we design a 3D map management scheme for edge-assisted mobile augmented reality (MAR) to support the pose estimation of individual MAR device, which uploads camera frames to an edge server. Our objective is to minimize the pose estimation uncertainty of the MAR device by periodically selecting a proper set of camera frames for uploading to update the 3D map. To address the challenges of the dynamic uplink data rate and the time-varying pose of the MAR device, we propose a digital twin (DT)-based approach to 3D map management. First, a DT is created for the MAR device, which emulates 3D map management based on predicting subsequent camera frames. Second, a model-based reinforcement learning (MBRL) algorithm is developed, utilizing the data collected from both the actual and the emulated data to manage the 3D map. With extensive emulated data provided by the DT, the MBRL algorithm can quickly provide an adaptive map management policy in a highly dynamic environment. Simulation results demonstrate that the proposed DT-based 3D map management outperforms benchmark schemes by achieving lower pose estimation uncertainty and higher data efficiency in dynamic environments.

cs.NI↗
arXiv2023

Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks.

cs.NI↗
arXiv2023

Toward Immersive Communications in 6G

The sixth generation (6G) networks are expected to enable immersive communications and bridge the physical and the virtual worlds. Integrating extended reality, holography, and haptics, immersive communications will revolutionize how people work, entertain, and communicate by enabling lifelike interactions. However, the unprecedented demand for data transmission rate and the stringent requirements on latency and reliability create challenges for 6G networks to support immersive communications. In this survey article, we present the prospect of immersive communications and investigate emerging solutions to the corresponding challenges for 6G. First, we introduce use cases of immersive communications, in the fields of entertainment, education, and healthcare. Second, we present the concepts of immersive communications, including extended reality, haptic communication, and holographic communication, their basic implementation procedures, and their requirements on networks in terms of transmission rate, latency, and reliability. Third, we summarize the potential solutions to addressing the challenges from the aspects of communication, computing, and networking. Finally, we discuss future research directions and conclude this study.

cs.NI↗
arXiv2023

Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework

In this paper, we address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data to help creating and rendering the digital copy of the physical world in the Metaverse. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. Nevertheless, mechanisms to hire sensing IoT devices to share their data with the VSP and then deliver the constructed digital twin to the Metaverse users are vulnerable to adverse selection problem. The adverse selection problem, which is caused by information asymmetry between the system entities, becomes harder to solve when the private information of the different entities are multi-dimensional. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem. To demonstrate the effectiveness of our algorithm, we conduct extensive simulations and measure several key performance metrics of the contract for the Metaverse. Our results show that our designed iterative contract is able to incentivize the participants to interact truthfully, which maximizes the profit of the VSP with minimal individual rationality (IR) and incentive compatibility (IC) violation rates. Furthermore, the proposed learning-based iterative contract framework has limited access to the private information of the participants, which is to the best of our knowledge, the first of its kind in addressing the problem of adverse selection in incentive mechanisms.

cs.AI↗
arXiv2022

A Survey on Metaverse: Fundamentals, Security, and Privacy

Metaverse, as an evolving paradigm of the next-generation Internet, aims to build a fully immersive, hyper spatiotemporal, and self-sustaining virtual shared space for humans to play, work, and socialize. Driven by recent advances in emerging technologies such as extended reality, artificial intelligence, and blockchain, metaverse is stepping from science fiction to an upcoming reality. However, severe privacy invasions and security breaches (inherited from underlying technologies or emerged in the new digital ecology) of metaverse can impede its wide deployment. At the same time, a series of fundamental challenges (e.g., scalability and interoperability) can arise in metaverse security provisioning owing to the intrinsic characteristics of metaverse, such as immersive realism, hyper spatiotemporality, sustainability, and heterogeneity. In this paper, we present a comprehensive survey of the fundamentals, security, and privacy of metaverse. Specifically, we first investigate a novel distributed metaverse architecture and its key characteristics with ternary-world interactions. Then, we discuss the security and privacy threats, present the critical challenges of metaverse systems, and review the state-of-the-art countermeasures. Finally, we draw open research directions for building future metaverse systems.

cs.CR↗