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Found 236 papers, 21 papers with code
Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework
In this work, we develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of large language models (LLMs) specifically for wireless communication applications.
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach
This integration, however, introduces complex challenges in designing coherent CPPS, particularly as few studies concurrently address the deployment of physical layers and communication connections in the cyber layer.
Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning
The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring.
Movable Antenna-Assisted Integrated Sensing and Communication Systems
Then, we develop an optimization framework aimed at maximizing the sensing signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the receive beamforming vector, the transmit beamforming matrix, and the positions of MAs while meeting the minimum SINR requirement for each user.
Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction
The key idea is that we interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features.
Energy-Efficient RIS-Aided Cell-Free Massive MIMO Systems: Application, Opportunities, and Challenges
Reconfigurable intelligent surfaces (RIS)-assisted cell-free massive multiple-input multiple-output (CF mMIMO) systems have emerged as a promising technology for sixth-generation communication systems.
Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters
The scheme effectively prevents NILM models from violating appliance-level privacy, while also ensuring accurate billing calculation for users.
Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities
The first stage, AI for Network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences.
Adaptive Pruning for Large Language Models with Structural Importance Awareness
The recent advancements in large language models (LLMs) have significantly improved language understanding and generation capabilities.
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks
The Space-Air-Ground Integrated Network (SAGIN) framework is a crucial foundation for future networks, where satellites and aerial nodes assist in computational task offloading.
Implicit Neural Compression of Point Clouds
Our approach employs two coordinate-based neural networks to implicitly represent a voxelized point cloud: the first determines the occupancy status of a voxel, while the second predicts the attributes of occupied voxels.
GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks
We build GDSG as a multi-task diffusion model utilizing a Graph Neural Network (GNN) to acquire the distribution of high-quality solutions.
UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks
Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously.
Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access
Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency.
Multi-Functional RIS Integrated Sensing and Communications for 6G Networks
We propose a MF-RIS-enabled multi-user and multi-target ISAC system, and formulate an optimization problem to maximize the signal-to-interference-plus-noise ratio (SINR) of sensing targets.
Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study
This article systemically explores the potential and effectiveness of GenAI-driven data augmentation in wireless networks.
Space-Air-Ground Integrated MEC-Assisted Industrial Cyber-Physical Systems: An Online Decentralized Optimization Approach
Cloud computing and edge/fog computing are playing a pivotal role in driving the transformation of industrial cyber-physical systems (ICPS) towards greater intelligence and automation by providing high-quality computation offloading services to Internet of Things devices (IoTDs).
Integrated Location Sensing and Communication for Ultra-Massive MIMO With Hybrid-Field Beam-Squint Effect
The advent of ultra-massive multiple-input-multiple output systems holds great promise for next-generation communications, yet their channels exhibit hybrid far- and near- field beam-squint (HFBS) effect.
Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services
We subsequently introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics.
Supervised Score-Based Modeling by Gradient Boosting
We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy.
Diffusion-based Auction Mechanism for Efficient Resource Management in 6G-enabled Vehicular Metaverses
In 6G-enable Vehicular Metaverses, vehicles are represented by Vehicle Twins (VTs), which serve as digital replicas of physical vehicles to support real-time vehicular applications such as large Artificial Intelligence (AI) model-based Augmented Reality (AR) navigation, called VT tasks.
Unauthorized UAV Countermeasure for Low-Altitude Economy: Joint Communications and Jamming based on MIMO Cellular Systems
We first formulate a joint communication and jamming (JCJ) problem, relaxing it through semi-definite relaxation (SDR) to obtain a tractable semi-definite programming (SDP) problem, with SDR providing an essential step toward simplifying the complex JCJ design.
Generative AI Enabled Matching for 6G Multiple Access
Then, we propose a framework based on generative diffusion models (GDMs) that iteratively denoises toward reward maximization to generate a matching strategy that meets specific requirements.
KANsformer for Scalable Beamforming
This paper proposes an unsupervised deep-learning (DL) approach by integrating transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems.
Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array
This paper reveals the potential of movable antennas in enhancing anti-jamming communication.
MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services
To comprehensively evaluate the contribution of locally trained learning models provided by MUs to AR services, we design a new immersion metric that captures service immersion by considering the freshness and accuracy of learning models, as well as the amount and potential value of raw data used for training.
STAR-RIS-Enabled Full-Duplex Integrated Sensing and Communication System
With the challenging limitations of traditional SIC approaches, this paper proposes a novel simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled FD ISAC system, where STAR-RIS enhances simultaneous communication and target sensing and reduces self-interference (SI) to a level comparable to traditional SIC approaches.
Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning
For that, we develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters compared to DRL thanks to the quantum superposition and quantum entanglement principles.
DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation
This model can be generated and fine-tuned in real time to achieve the objective and meet the constraints of target intent networks, significantly reducing state information exposure during wireless communication.
Wireless Human-Machine Collaboration in Industry 5.0
Building on this model, we propose a stochastic cycle-cost-based approach to derive a stability condition for the WHMC system, expressed in terms of wireless channel statistics, human dynamics, and control parameters.
Communication-Control Codesign for Large-Scale Wireless Networked Control Systems
Wireless Networked Control Systems (WNCSs) are essential to Industry 4. 0, enabling flexible control in applications, such as drone swarms and autonomous robots.
Two Birds With One Stone: Enhancing Communication and Sensing via Multi-Functional RIS
Furthermore, we present two schemes that utilize MF-RISs to enhance the performance of integrated sensing and communication (ISAC).
GNN-Enabled Optimization of Placement and Transmission Design for UAV Communications
This paper applies graph neural networks (GNN) in UAV communications to optimize the placement and transmission design.
Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks
Embodied twins are digital models of these embodied agents, with various embodied AI twins for intelligent applications in cyberspace.
Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks
This system comprises a gating network and multiple experts, each specializing in different security challenges.
The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles
With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications.
Robust Beamforming Design for Near-Field DMA-NOMA mmWave Communications With Imperfect Position Information
For millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communication systems, we propose an innovative near-field (NF) transmission framework based on dynamic metasurface antenna (DMA) technology.
Delay Minimization for Movable Antennas-Enabled Anti-Jamming Communications With Mobile Edge Computing
The increase in DoF enhances the system's anti-jamming capabilities and reduces system delay.
Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
We review the current state of LM agents, the key technologies enabling LM agent collaboration, and the security and privacy challenges they face during cooperative operations.
Signal Detection in Near-field Communication with Unknown Noise Characteristics: A Diffusion Model Method
Then, we proposed a Maximum Likelihood Estimation Diffusion Detector (MLEDD) aiming at learning the distribution of unknown noise.
Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers
Without knowledge of the attack target, adversarial perturbations are injected to the pilots, poisoning the online deep receiver and impairing its ability to adapt to dynamic channels and nonlinear effects.
Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse
Federated learning (FL) has emerged as a promising technique for collaboratively training AI models while preserving data privacy.
Generative AI based Secure Wireless Sensing for ISAC Networks
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.
Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems
In this paper, we first develop a GenAI-driven DT architecture in ICPSs.
Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels
Notably, we derive closed-form achievable SE expressions for centralized and distributed BE-structured combining schemes.
Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II
Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem.
S-RAN: Semantic-Aware Radio Access Networks
Semantic communication (SemCom) has been a transformative paradigm, emphasizing the precise exchange of meaningful information over traditional bit-level transmissions.
Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehicles
In response, this paper introduces a novel and cost-effective TFE framework that leverages sparse PVD and improves accuracy by applying the spatial-temporal generative artificial intelligence (GAI) framework.
Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses
The vehicular metaverse is envisioned as a blended immersive domain that promises to bring revolutionary changes to the automotive industry.
Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification
Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication efficiency stemming from the considerable size of parameters and extensive communication rounds.
Channel Twinning: An Enabler for Next-Generation Ubiquitous Wireless Connectivity
The emerging concept of channel twinning (CT) has great potential to become a key enabler of ubiquitous connectivity in next-generation (xG) wireless systems.
QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing: An Online Joint Optimization Approach
Then, we formulate a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy consumption constraints of UAVs.
Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph
To address this, we propose Personalized Federated knowledge graph Embedding with client-wise relation Graph (PFedEG), a novel approach that employs a client-wise relation graph to learn personalized embeddings by discerning the semantic relevance of embeddings from other clients.
Efficient Prompting for LLM-based Generative Internet of Things
Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently.
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Predicting Cascading Failures with a Hyperparametric Diffusion Model
In this paper, we study cascading failures in power grids through the lens of information diffusion models.
DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
In this paper, we formulate the problem of joint DNN partitioning, task offloading, and resource allocation in VEC as a dynamic long-term optimization.
Revolutionizing Wireless Networks with Self-Supervised Learning: A Pathway to Intelligent Communications
With the rapid proliferation of mobile devices and data, next-generation wireless communication systems face stringent requirements for ultra-low latency, ultra-high reliability, and massive connectivity.
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.
Optimizing 6G Integrated Sensing and Communications (ISAC) via Expert Networks
Integrated Sensing and Communications (ISAC) is one of the core technologies of 6G, which combines sensing and communication functions into a single system.
Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases
We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms.
Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network
Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming.
From Single to Multi-Functional RIS: Architecture, Key Technologies, Challenges, and Applications
Although reconfigurable intelligent surfaces (RISs) have demonstrated the potential to boost network capacity and expand coverage by adjusting their electromagnetic properties, existing RIS architectures have certain limitations, such as double-fading attenuation and restricted half-space coverage.
Multi-Objective Optimization-Based Waveform Design for Multi-User and Multi-Target MIMO-ISAC Systems
In this paper, we investigate the waveform design problem in a downlink multi-user and multi-target ISAC system under different C&S performance preferences.
Sparse Attention-driven Quality Prediction for Production Process Optimization in Digital Twins
By iteratively mapping the real-world data reflecting equipment operation status and product quality indicators in the digital twin, we adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks.
Point Cloud Compression with Implicit Neural Representations: A Unified Framework
By feeding the coordinates of these voxels into the respective networks, we reconstruct the geometry and attribute components of the original point cloud.
Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework
As the demands for immediate and effective responses increase in both civilian and military domains, the unmanned aerial vehicle (UAV) swarms emerge as effective solutions, in which multiple cooperative UAVs can work together to achieve specific goals.
Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data
Integrated sensing and communications (ISAC) is pivotal for 6G communications and is boosted by the rapid development of reconfigurable intelligent surfaces (RISs).
Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models
In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT.
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.
ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction
However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i. e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush.
Personalized Wireless Federated Learning for Large Language Models
Large Language Models (LLMs) have revolutionized natural language processing tasks.
Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers
To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images).
AI-Empowered RIS-Assisted Networks: CV-Enabled RIS Selection and DNN-Enabled Transmission
This paper investigates artificial intelligence (AI) empowered schemes for reconfigurable intelligent surface (RIS) assisted networks from the perspective of fast implementation.
Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation
Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks.
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.
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision
Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable.
Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning
Specifically, it is found that DCB enables terminals that cannot reach the uplink achievable threshold to achieve efficient direct uplink transmission, which thus reveals that DCB is an effective solution for enabling direct ground-space communications.
UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning
To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs.
BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling
Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries.
A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles
Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models.
The Frontier of Data Erasure: Machine Unlearning for Large Language Models
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation.
TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing
Since the problem is a non-convex and NP-hard mixed integer nonlinear programming (MINLP), we propose a two-timescale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach for solving the problem.
Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys.
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.
Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks
Extensive simulation results are presented to demonstrate the effectiveness of the proposed random aggregate beamforming-based scheme as well as the refined method.
Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving
Our method consists of two steps, i. e. training DRL models with a group sparse regularizer and removing unimportant neurons with a dynamic pruning threshold.
Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach
To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles.
Collaborative Deep Reinforcement Learning for Resource Optimization in Non-Terrestrial Networks
Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services.
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued.
Scalable Federated Unlearning via Isolated and Coded Sharding
Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models.
Blockchain-enabled Trustworthy Federated Unlearning
However, existing works require central servers to retain the historical model parameters from distributed clients, such that allows the central server to utilize these parameters for further training even, after the clients exit the training process.
Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study
Generative Artificial Intelligence (GAI) has recently emerged as a promising solution to address critical challenges of blockchain technology, including scalability, security, privacy, and interoperability.
Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey
The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare, attracting extensive attentions to IoT-healthcare services.
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.
Distributed Task-Oriented Communication Networks with Multimodal Semantic Relay and Edge Intelligence
In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence.
Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach
To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses.
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.
Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing
Furthermore, in multi-target scenarios, the fall detection achieves an average true positive rate of 89. 56% and a false positive rate of 11. 78%, demonstrating its importance in enhancing indoor wireless sensing capabilities.
Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications
Furthermore, GAI algorithms also play an important role in the management of SemCom networks.
Large Language Model Enhanced Multi-Agent Systems for 6G Communications
The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e. g., network optimization and management by allowing users to input task requirements to LLMs by nature language.
Generative AI for Physical Layer Communications: A Survey
The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity.
Few-Shot Recognition and Classification Framework for Jamming Signal: A CGAN-Based Fusion CNN Approach
Subject to intricate environmental variables, the precise classification of jamming signals holds paramount significance in the effective implementation of anti-jamming strategies within communication systems.
GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks
This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users' individual data rate requirements and the power budget of the base station.
Exploring Federated Unlearning: Analysis, Comparison, and Insights
The increasing demand for privacy-preserving machine learning has spurred interest in federated unlearning, which enables the selective removal of data from models trained in federated systems.
From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks
Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making.
A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication
With the significant advances in AI-generated content (AIGC) and the proliferation of mobile devices, providing high-quality AIGC services via wireless networks is becoming the future direction.
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.
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating pro-social applications.
UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper
Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network.
Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective
In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems.
TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework
Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime.
Knowledge Base Aware Semantic Communication in Vehicular Networks
Semantic communication (SemCom) has recently been considered a promising solution for the inevitable crisis of scarce communication resources.
Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks
Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT).
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.
Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue
Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost.
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.
Beyond Reality: The Pivotal Role of Generative AI in the Metaverse
This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse, transforming it into a dynamic, immersive, and interactive virtual world.
A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC
To promote the development of this new breed of paradigm, in this article, we propose a system architecture of mobile AIGC-driven HDT and highlight the corresponding design requirements and challenges.
Federated Learning-Empowered AI-Generated Content in Wireless Networks
Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models.
Cost-Effective Task Offloading Scheduling for Hybrid Mobile Edge-Quantum Computing
We develop cost-effective designs for both task offloading mode selection and resource allocation, subject to the individual link latency constraint guarantees for mobile devices, while satisfying the required success ratio for their computation tasks.