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Found 33 papers, 4 papers with code
Interactive Generative AI Agents for Satellite Networks through 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 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.
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.
Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study
By integrating Generative Artificial Intelligence (GAI) with mobile edge networks, generative mobile edge networks possess immense potential to enhance the intelligence and efficiency of wireless communication networks.
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.
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.
Service Reservation and Pricing for Green Metaverses: A Stackelberg Game Approach
A single-leader multi-follower Stackelberg game is formulated between the MSP and users while each user optimizes an offloading probability to minimize the weighted sum of time, energy consumption and monetary cost.
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.
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.
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.
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.
Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework
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.
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.
Knowledge-Aware Semantic Communication System Design and Data Allocation
In SemCom systems, only the relevant keywords from the data are extracted and used for transmission.
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.
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.
Reconfigurable Intelligent Surface-Aided Joint Radar and Covert Communications: Fundamentals, Optimization, and Challenges
Thus, we first apply covert communication into JRC and propose a joint radar and covert communication (JRCC) system to achieve high spectrum utilization and secure data transmission simultaneously.
Design and Implementation of 5.8GHz RF Wireless PowerTransfer System
In this paper, we present a 5. 8 GHz radio-frequency (RF) wireless power transfer (WPT) system that consists of 64 transmit antennas and 16 receive antennas.
Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning
This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network.
Reconfigurable Intelligent Surface-Aided Wireless Power Transfer Systems: Analysis and Implementation
Reconfigurable intelligent surface (RIS) is a promising technology for RF wireless power transfer (WPT) as it is capable of beamforming and beam focusing without using active and power-hungry components.
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications
Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments.
Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks
Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity and inexact channel information.
Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey
First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems.
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
However, the issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency.
Toward an Automated Auction Framework for Wireless Federated Learning Services Market
In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning.
Computer Science and Game Theory
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.
Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach
In this letter, we propose to adopt a deep- Q learning algorithm that allows the server to learn and find optimal decisions without any a priori knowledge of network dynamics.
Networking and Internet Architecture
Joint Service Pricing and Cooperative Relay Communication for Federated Learning
However, the learning process of the existing federated learning platforms rely on the direct communication between the model owner, e. g., central cloud or edge server, and the mobile devices for transferring the model update.
Cryptography and Security Computer Science and Game Theory
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e. g., decisions or actions, given their states when the state and action spaces are small.
Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks
To coordinate the transmission of multiple secondary transmitters, the secondary gateway needs to schedule the backscattering time, energy harvesting time, and transmission time among them.
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
