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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

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TechRxiv

TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

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arXiv 2024年

作者： Liu, Guangyuan Van Huynh, Nguyen Du, Hongyang Hoang, Dinh Thai Niyato, Dusit Zhu, Kun Kang, Jiawen Xiong, Zehui Jamalipour, Abbas In Kim, Dong The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Department of Electrical Engineering and Electronics University of Liverpool Liverpool L69 3GJ United Kingdom The School of Electrical and Data Engineering University of Technology Sydney Australia The School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions. © 2024, CC BY.

关键词： Metadata

AImotion Challenge Results: a Framework for AirSim Autonomous Vehicles and Motion Replication

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AImotion Challenge Results: a Framework for AirSim Autonomou...

Computers and automation (CompAuto), International Conference on

作者： Bruno J. Souza Lucas C. de Assis Dominik Rößle Roberto Z. Freire Daniel Cremers Torsten Schön Munir Georges Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Control and Automation Eng. Department Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Technische Hochschule Ingolstadt AImotion Bavaria Institute Ingolstadt Germany Department of Informatics Technical University of Munich Munich Germany

ISBN: (纸本)9781665481953

The use of simulation environments is becoming more significant in the development of autonomous cars, as it allows for the simulation of high-risk situations while also being less expensive. In this paper, we presented a framework that allows the creation of an autonomous vehicle in the AirSim simulation environment and then transporting the simulated movements to the TurtleBot. To maintain the car in the correct direction, computer vision techniques such as object detection and lane detection were assumed. The vehicle's speed and steering are both determined by Proportional-Integral-Derivative (PID) controllers. A virtual personal assistant was developed employing natural language processing to allow the user to interact with the environment, providing movement instructions related to the vehicle's direction. Additionally, the conversion of the simulator movements for a robot was implemented to test the proposed system in a practical experiment. A comparison between the real position of the robot and the position of the vehicle in the simulated environment was considered in this study to evaluate the performance of the algorithms.

关键词： Computer vision Lane detection Robot kinematics Atmospheric modeling Computational modeling Virtual environments Natural language processing

Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin Poor, H. Vincent The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices. Our code is available at https://***/HongyangDu/DistributedDiffusion. © 2023, CC BY.

关键词： Wireless networks

Power-and Current-Based Control of Distributed Inverters in Low-Voltage Microgrids: Considerations in Relation to Classic Droop Control 15

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Power-and Current-Based Control of Distributed Inverters in ...

15th International Conference on Ecological Vehicles and Renewable Energies, EVER 2020

作者： Marafao, Fernando P. Alonso, Augusto M. S. Gothner, Fredrik Tedeschi, Elisabetta Brandao, Danilo I. Sao Paulo State University Group of Automation and Int. Systems Sorocaba18087-180 Brazil Norwegian University of Science and Technology Department of Electric Power Engineering Trondheim7034 Norway Federal University of Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte31270-901 Brazil

ISBN: (纸本)9781728156415

This paper addresses the matter of active and reactive power sharing among distributed inverters in low-voltage microgrids, particularly highlighting the peculiarities of two strategies of coordinated control based on a master/slave architecture, namely Power-and Current-Based Control, in comparison to the classic droop control approach. It is demonstrated that the Power-and Current-Based Control strategies present distinct operational features in relation to classic droop control, mostly concerning how active and reactive power are shared among inverters. Additionally, it is shown how the strategies differ in steady-state operation and response time while comparing to classic droop-based methods. Discussions are presented based on simulation results using Matlab/Simulink, comprising two distributed inverters in a low-voltage microgrid and considering different operational scenarios, such as load changes, existence of linear and nonlinear loads, as well as line impedance variations. Experimental results are presented to validate the expected behavior of one of the strategies. © 2020 IEEE.

关键词： Electric inverters

Experimental Analyses of the Combustion Instability Parameters in an Ammonia/Methane Fuelled Low Eddy Premix Burner for Gas Turbine Power Generation

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SSRN

SSRN 2022年

作者： Ilbas, Mustafa Kumuk, Osman Alabas, Bugrahan Department of Energy Systems Engineering Faculty of Technology Gazi University Teknikokullar Ankara Turkey Graduate School of Natural and Applied Science Gazi University Teknikokullar Ankara Turkey Unmanned Aerial Vehicle Technology and Operatorship Program Electronic and Automation Department Iskenderun Vocational School of Higher Education Iskenderun Technical University Hatay Turkey Department of Aeronautical Engineering Faculty of Aeronautics and Astronautics Erciyes University Kayseri Turkey

The main factor that directs scientists to work on alternative energy sources is the environmental damage caused by the waste gases generated by the use of fossil fuels. Since ammonia is a carbonless substance and contains hydrogen atoms per unit volume, it can be used as an alternative fuel. In this study, experimental analyzes were carried out to examine the combustion instability properties of methane-ammonia mixed fuels for a low eddy and premix burner. The aim of this study is to determine the effect of ammonia addition on combustion instability with different compelling acoustic frequencies, dynamic pressure amplitude and light emission intensity. In addition, the effects of ammonia addition to methane fuel gas on temperature and emissions were also investigated. Pure methane and three different methane-ammonia mixtures (by volume 100% CH4, 90% CH 4 - 10 NH3, 80% CH4 - 20% NH3 and 70% CH4-30 NH3) as fuel at 0.7 equivalents and with three different acoustics have been used for analyzes. The burner power was determined as 5 kW and the excitation frequencies of the combustion chamber were determined as 95, 160 and 300 Hz. In this study, as the ammonia addition increased, there was an increase in the flame instability values up to 30% NH3, especially at the 95 Hz frequency, where the highest distortion effect occurred. It was observed that there was no significant change in the combustion performance with the addition of ammonia to methane fuel. The predicted NOX emission levels were found to increase significantly due to nitrogen bound to ammonia. However, it is seen that the NOx emission levels in the exhaust measurements are not very high (Maximum 560 ppm). Therefore, it can be concluded that ammonia supplemented methane fuels have significant potential as an alternative fuel. © 2022, The Authors. All rights reserved.

关键词： Ammonia

Robust Current Control of Three-Phase Grid-Connected Converters by Using the ADRC Method 16

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Robust Current Control of Three-Phase Grid-Connected Convert...

16th Brazilian Power Electronics Conference, COBEP 2021

作者： Campos De Araujo, Diana Alves Moor Neto, Joao Amin Do Brasil, Thiago Americano Gouvea, Josiel Alves Lopes Zachi, Alessandro Rosa Electrical Engineering Graduate Program Centro Fed. de Educ. Tecn. Celso Suckow da Fonseca CEFET/RJ Rio de Janeiro Brazil Centro Fed. de Educ. Tecn. Celso Suckow da Fonseca CEFET/RJ Department of Electrical Engineering Rio de Janeiro Brazil Centro Fed. de Educ. Tecn. Celso Suckow da Fonseca CEFET/RJ Department of Control Systems and Automation Rio de Janeiro Brazil

ISBN: (纸本)9781665421294

This paper addresses the control design of a current mode control for a three-phase voltage source converter. A variant of the Active Disturbance Rejection Control (ADRC) scheme is proposed for comparison with the classical inner control approach. The ADRC method has a straightforward design procedure and has well-known abilities for disturbance rejection and robustness to parametric uncertainties of the system. Theoretical studies are presented and simulation results are discussed by using EMTP-RV environment considering the system Synchronous Reference Frame Control, whose modeling was extracted from the analysis performed in a classical refer-ence. © 2021 IEEE.

关键词： Electric current control