Built on

H. Xiao, C. Xu, Y. Ma, S. Yang, L. Zhong, and G.-M. Muntean, “Edge intelligence: A computational task offloading scheme for dependent IoT application,” IEEE Trans. Wireless Commun. , vol. 21, no. 9, pp. 7222–7237, Sep. 2022
2022
Earlier work this paper cites.
Y. Choukroun and L. Wolf, “Denoising diffusion error correction codes,” arXiv preprint arXiv:2209.13533 , 2022
Original
2022
Earlier work this paper cites.
A. Liu, Z. Huang, M. Li, Y. Wan, W. Li, T. X. Han, C. Liu, R. Du, D. K. P. Tan, J. Lu, Y. Shen, F. Colone, and K. Chetty, “A survey on fundamental limits of integrated sensing and communication,” IEEE Commun. Surv. Tut. , vol. 24, no. 2, pp. 994–1034, Feb. 2022
2022
Earlier work this paper cites.
H. Du, R. Zhang, Y. Liu, J. Wang, Y. Lin, Z. Li, D. Niyato, J. Kang, Z. Xiong, S. Cui, B. Ai, H. Zhou, and D. I. Kim, “Beyond deep reinforcement learning: A tutorial on generative diffusion models in network optimization,” arXiv preprint arXiv:2308.05384 , 2023
Original
2023
Earlier work this paper cites.
D. Baidoo-Anu and L. O. Ansah, “Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning,” J. AI , vol. 7, no. 1, pp. 52–62, Jan. 2023
2023
Earlier work this paper cites.

Similar

M. Xu, H. Du, D. Niyato, J. Kang, Z. Xiong, S. Mao, Z. Han, A. Jamalipour, D. I. Kim, V. Leung, and H. V. Poor, “Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services,” arXiv preprint arXiv:2303.16129 , 2023
Original
2023
Cited alongside, same era.
Y. Zuo, J. Guo, N. Gao, Y. Zhu, S. Jin, and X. Li, “A survey of blockchain and artificial intelligence for 6G wireless communications,” IEEE Commun. Surv. Tut. , to appear, 2023
2023
Cited alongside, same era.
H. Du, Z. Li, D. Niyato, J. Kang, Z. Xiong, H. Huang, and S. Mao, “Generative AI-aided optimization for AI-generated content (AIGC) services in edge networks,” arXiv preprint arXiv:2303.13052 , 2023
Original
2023
Cited alongside, same era.
H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, and D. I. Kim, “AI-generated incentive mechanism and full-duplex semantic communications for information sharing,” IEEE J. Sel. Areas Commun. , to appear, 2023
2023
Cited alongside, same era.

Then

H. Du, R. Zhang, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, X. S. Shen, and H. V. Poor, “Exploring collaborative distributed diffusion-based AI-generated content (AIGC) in wireless networks,” IEEE Netw. , no. 99, pp. 1–8, 2023
2023
Closest in time.
H. Du, G. Liu, D. Niyato, J. Zhang, J. Kang, Z. Xiong, B. Ai, and D. I. Kim, “Generative AI-aided joint training-free secure semantic communications via multi-modal prompts,” arXiv preprint arXiv:2309.02616 , 2023
Original
2023
Closest in time.
L. Song, B. Di, H. Zhang, and Z. Han, Aerial Access Networks: Integration of UAVs, HAPs, and Satellites . Cambridge University Press, 2023
2023
Closest in time.
T. Han, J. Tang, Q. Yang, Y. Duan, Z. Zhang, and Z. Shi, “Generative model-based highly efficient semantic communication approach for image transmission,” in IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP) , May 2023, pp. 1–5
2023
Closest in time.

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2023

The Age of Generative AI and AI-Generated Everything

Du, Hongyang, Niyato, Dusit, Kang, Jiawen et al.

Understand

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.

Built on

H. Xiao, C. Xu, Y. Ma, S. Yang, L. Zhong, and G.-M. Muntean, “Edge intelligence: A computational task offloading scheme for dependent IoT application,” IEEE Trans. Wireless Commun. , vol. 21, no. 9, pp. 7222–7237, Sep. 2022
2022
Earlier work this paper cites.
Y. Choukroun and L. Wolf, “Denoising diffusion error correction codes,” arXiv preprint arXiv:2209.13533 , 2022
Original
2022
Earlier work this paper cites.
A. Liu, Z. Huang, M. Li, Y. Wan, W. Li, T. X. Han, C. Liu, R. Du, D. K. P. Tan, J. Lu, Y. Shen, F. Colone, and K. Chetty, “A survey on fundamental limits of integrated sensing and communication,” IEEE Commun. Surv. Tut. , vol. 24, no. 2, pp. 994–1034, Feb. 2022
2022
Earlier work this paper cites.
H. Du, R. Zhang, Y. Liu, J. Wang, Y. Lin, Z. Li, D. Niyato, J. Kang, Z. Xiong, S. Cui, B. Ai, H. Zhou, and D. I. Kim, “Beyond deep reinforcement learning: A tutorial on generative diffusion models in network optimization,” arXiv preprint arXiv:2308.05384 , 2023
Original
2023
Earlier work this paper cites.
D. Baidoo-Anu and L. O. Ansah, “Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning,” J. AI , vol. 7, no. 1, pp. 52–62, Jan. 2023
2023
Earlier work this paper cites.

Similar

M. Xu, H. Du, D. Niyato, J. Kang, Z. Xiong, S. Mao, Z. Han, A. Jamalipour, D. I. Kim, V. Leung, and H. V. Poor, “Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services,” arXiv preprint arXiv:2303.16129 , 2023
Original
2023
Cited alongside, same era.
Y. Zuo, J. Guo, N. Gao, Y. Zhu, S. Jin, and X. Li, “A survey of blockchain and artificial intelligence for 6G wireless communications,” IEEE Commun. Surv. Tut. , to appear, 2023
2023
Cited alongside, same era.
H. Du, Z. Li, D. Niyato, J. Kang, Z. Xiong, H. Huang, and S. Mao, “Generative AI-aided optimization for AI-generated content (AIGC) services in edge networks,” arXiv preprint arXiv:2303.13052 , 2023
Original
2023
Cited alongside, same era.
H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, and D. I. Kim, “AI-generated incentive mechanism and full-duplex semantic communications for information sharing,” IEEE J. Sel. Areas Commun. , to appear, 2023
2023
Cited alongside, same era.

Then

H. Du, R. Zhang, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, X. S. Shen, and H. V. Poor, “Exploring collaborative distributed diffusion-based AI-generated content (AIGC) in wireless networks,” IEEE Netw. , no. 99, pp. 1–8, 2023
2023
Closest in time.
H. Du, G. Liu, D. Niyato, J. Zhang, J. Kang, Z. Xiong, B. Ai, and D. I. Kim, “Generative AI-aided joint training-free secure semantic communications via multi-modal prompts,” arXiv preprint arXiv:2309.02616 , 2023
Original
2023
Closest in time.
L. Song, B. Di, H. Zhang, and Z. Han, Aerial Access Networks: Integration of UAVs, HAPs, and Satellites . Cambridge University Press, 2023
2023
Closest in time.
T. Han, J. Tang, Q. Yang, Y. Duan, Z. Zhang, and Z. Shi, “Generative model-based highly efficient semantic communication approach for image transmission,” in IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP) , May 2023, pp. 1–5
2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

X. Huang, P. Li, H. Du, J. Kang, D. Niyato, D. I. Kim, and Y. Wu, “Federated learning-empowered AI-generated content in wireless networks,” arXiv preprint arXiv:2307.07146 , 2023

Original

2023

Cited alongside, same era.

Y. Liu, Z. Wang, J. Xu, C. Ouyang, X. Mu, and R. Schober, “Near-field communications: A tutorial review,” arXiv preprint arXiv:2305.17751 , 2023

Original

2023

Closest in time.