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Found 56 papers, 4 papers with code
Pilot-Aided Joint Time Synchronization and Channel Estimation for OTFS
This letter proposes a pilot-aided joint time synchronization and channel estimation (JTSCE) algorithm for orthogonal time frequency space (OTFS) systems.
FAST-GSC: Fast and Adaptive Semantic Transmission for Generative Semantic Communication
Aiming to reduce task latency, our communication mechanism enables fast semantic transmission by parallelizing the processes of semantic extraction at the transmitter and inference at the receiver.
Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach
In this mechanism, we propose a two-stage matching for vehicular users and Metaverse service providers in multi-attribute resource markets.
Edge Intelligence Optimization for Large Language Model Inference with Batching and Quantization
Specifically, with the deployment of the batching technique and model quantization on resource-limited edge devices, we formulate an inference model for transformer decoder-based LLMs.
Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey
Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions.
Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments
Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i. e., the subflow size.
Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission
Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem.
Agent-driven Generative Semantic Communication with Cross-Modality and Prediction
In contrast to the existing research on semantic communication (SemCom), which mainly focuses on either semantic extraction or semantic sampling, we seamlessly integrate both by jointly considering the intrinsic attributes of source information and the contextual information regarding the task.
Harnessing the Power of AI-Generated Content for Semantic Communication
Recognizing the transformative capabilities of AI-generated content (AIGC) technologies in content generation, this paper explores a pioneering approach by integrating them into SemCom to address the aforementioned challenges.
Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities
For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms.
Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems
In this context, we propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency, a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model to ensure precise monitoring based on users' activities.
When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment
AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various domains like healthcare, education, manufacturing, and entertainment.
Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts
The system jointly optimizes the diffusion step, jamming, and transmitting power with the aid of the generative diffusion models, enabling successful and secure transmission of the source messages.
Artificial Intelligence for Web 3.0: A Comprehensive Survey
Then, we thoroughly analyze the current state of AI technology applications in the four layers of Web 3. 0 and offer some insights into its potential future development direction.
Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach
The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.
Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation
Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation.
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness
Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services.
Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction
Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs. Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed integer programming problem.
STAR-RIS-Assisted Privacy Protection in Semantic Communication System
Semantic communication (SemCom) has emerged as a promising architecture in the realm of intelligent communication paradigms.
Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses
In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool.
DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation
To eliminate the influences of adverse weather conditions, we propose a dual attention and dual frequency-guided dehazing network (termed DADFNet) for real-time visibility enhancement.
Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study
With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022.
Guiding AI-Generated Digital Content with Wireless Perception
Experiments results verify the effectiveness of the WP-AIGC framework, highlighting its potential as a novel approach for guiding AI models in the accurate generation of digital content.
AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing
Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images.
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems.
Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse
Furthermore, we propose a multi-task enhanced auction-based mechanism to match and price AVs and MARs for RSUs to provision real-time and effective services.
Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks
To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.
When Quantum Information Technologies Meet Blockchain in Web 3.0
Therefore, in this paper, we introduce a quantum blockchain-driven Web 3. 0 framework that provides information-theoretic security for decentralized data transferring and payment transactions.
Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications
In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications.
Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding
To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced.
Multi-Resource Allocation for On-Device Distributed Federated Learning Systems
This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system.
Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis
Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G networks and applications, e. g., smart healthcare.
Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services
Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse.
Economics of Semantic Communication System: An Auction Approach
The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems.
Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services
With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i. e., attention prediction and QoE maximization.
STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network
—The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT).
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning
Since each client has a limited amount of computing resources, the problem of allocating computing resources into training and mining needs to be carefully addressed.
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones
This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model.
A Contract Theory based Incentive Mechanism for Federated Learning
It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients.
Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems
Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs).
Information Theory Signal Processing Information Theory
Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning
Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated.
Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications
This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs).
Cross-Layer Coordinated Attacks on Cyber-Physical Systems: A LQG Game Framework with Controlled Observations
On the other hand, the interactions between the attacker and the defender in the physical layer significantly impact the observation and jamming strategies.
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management
However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training.
Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework
This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation.
Cryptography and Security
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-\textit{k} selection to improve communication efficiency.
Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles
The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs.
Networking and Internet Architecture Signal Processing
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications.
Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning
In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous.
Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach
Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built.
Signal Processing Networking and Internet Architecture
Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications
As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments.
Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting
Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS.
Reliable Federated Learning for Mobile Networks
Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks.
Cryptography and Security
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e. g., mobile devices, to distributedly train and globally share models without revealing their local data.
A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks
This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks.
Cryptography and Security
Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach
However, a mechanism needs to be designed for edge resource allocation to maximize the revenue for the Edge Computing Service Provider and to ensure incentive compatibility and individual rationality is still open.
Computer Science and Game Theory
