The Age of Generative AI and AI-Generated Everything

被引:4
作者
Du, Hongyang [1 ]
Niyato, Dusit [1 ]
Kang, Jiawen [2 ,3 ]
Xiong, Zehui [4 ]
Zhang, Ping [5 ]
Cui, Shuguang [6 ,7 ]
Shen, Xuemin [8 ]
Mao, Shiwen [9 ]
Han, Zhu [10 ]
Jamalipour, Abbas [11 ]
Poor, H. Vincent [12 ]
Kim, Dong In [13 ]
机构
[1] Nanyang Technol Univ, Coll Comp & Data Sci, Singapore 639798, Singapore
[2] Guangdong Univ Technol, Sch Automat, Key Lab Intelligent Informat Proc & Syst Integrat, Minist Educ, Guangzhou 510006, Peoples R China
[3] Guangdong Univ Technol, Guangdong Hong Kong Macao Joint Lab Smart Discrete, Guangzhou 510006, Peoples R China
[4] Singapore Univ Technol & Design, Pillar Informat Syst Technol & Design, Singapore 487372, Singapore
[5] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[6] Chinese Univ Hong Kong, Future Network Intelligence Inst FNii, Sch Sci & Engn SSE, Shenzhen 518172, Peoples R China
[7] Chinese Univ Hong Kong, Guangdong Prov Key Lab Future Networks Intelligenc, Shenzhen 518172, Peoples R China
[8] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
[9] Auburn Univ, Dept Elect & Comp Engn, Auburn, AL 36849 USA
[10] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
[11] Univ Sydney, Sch Elect & Comp Engn, Sydney, NSW 2006, Australia
[12] Princeton Univ, Dept Elect & Comp Engn, Princeton, NJ 08544, Algeria
[13] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon, 16419, South Korea
来源
IEEE NETWORK | 2024年 / 38卷 / 06期
基金
美国国家科学基金会; 中国国家自然科学基金; 新加坡国家研究基金会;
关键词
Semantics; Generative AI; Resource management; Optimization; Laboratories; Communication system security; Adaptation models; Generative AI (GAI); networks; AI-generated everything (AIGC); generative diffusion model;
D O I
10.1109/MNET.2024.3422241
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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. It is hoped that this article will pave the way for subsequent future research aimed at fully unlocking the potential of GAI and networks.
引用
收藏
页码:501 / 512
页数:12
相关论文
共 15 条
  • [1] Baidoo-Anu D., 2023, Journal of AI, V7, P52, DOI [DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484, 10.61969/jai.1337500]
  • [2] Choukroun Y., 2023, P INT C LEARN REPR, P1
  • [3] Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts
    Du, Hongyang
    Liu, Guangyuan
    Niyato, Dusit
    Zhang, Jiayi
    Kang, Jiawen
    Xiong, Zehui
    Ai, Bo
    Kim, Dong In
    [J]. 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2024), 2024, : 12896 - 12900
  • [4] Diffusion-Based Reinforcement Learning for Edge-Enabled AI-Generated Content Services
    Du, Hongyang
    Li, Zonghang
    Niyato, Dusit
    Kang, Jiawen
    Xiong, Zehui
    Huang, Huawei
    Mao, Shiwen
    [J]. IEEE TRANSACTIONS ON MOBILE COMPUTING, 2024, 23 (09) : 8902 - 8918
  • [5] Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks
    Du, Hongyang
    Zhang, Ruichen
    Niyato, Dusit
    Kang, Jiawen
    Xiong, Zehui
    Kim, Dong In
    Shen, Xuemin
    Poor, H. Vincent
    [J]. IEEE NETWORK, 2024, 38 (03): : 178 - 186
  • [6] Du HY, 2024, Arxiv, DOI [arXiv:2308.05384, 10.1109/COMST.2024.3400011, DOI 10.1109/COMST.2024.3400011]
  • [7] AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing
    Du, Hongyang
    Wang, Jiacheng
    Niyato, Dusit
    Kang, Jiawen
    Xiong, Zehui
    Kim, Dong In
    [J]. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2023, 41 (09) : 2981 - 2997
  • [8] Han T., 2023, P IEEE INT C AC SPEE, P1
  • [9] Federated Learning-Empowered AI-Generated Content in Wireless Networks
    Huang, Xumin
    Li, Peichun
    Du, Hongyang
    Kang, Jiawen
    Niyato, Dusit
    Kim, Dong In
    Wu, Yuan
    [J]. IEEE NETWORK, 2024, 38 (05): : 304 - 313
  • [10] A Survey on Fundamental Limits of Integrated Sensing and Communication
    Liu, An
    Huang, Zhe
    Li, Min
    Wan, Yubo
    Li, Wenrui
    Han, Tony Xiao
    Liu, Chenchen
    Du, Rui
    Tan, Danny Kai Pin
    Lu, Jianmin
    Shen, Yuan
    Colone, Fabiola
    Chetty, Kevin
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2022, 24 (02): : 994 - 1034