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Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications.
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2023
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S. Hong, G. Lee, W. Jang, and S. Kim, “Improving sample quality of diffusion models using self-attention guidance,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2023, pp. 7462–7471
2023
Closest in time.
Y. Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, C. Miao, Xuemin, Shen, and A. Jamalipour, “Blockchain-empowered lifecycle management for AI-Generated Content (AIGC) products in edge networks,” IEEE Wireless Commun. , to appear, 2023
2023
Closest in time.
Y. Lin, Z. Gao, H. Du, D. Niyato, J. Kang, R. Deng, and X. S. Shen, “A unified blockchain-semantic framework for wireless edge intelligence enabled web 3.0,” IEEE Wirel Commun , 2023
2023
Closest in time.
H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, J. Zhang, and X. Shen, “Semantic communications for wireless sensing: RIS-aided encoding and self-supervised decoding,” IEEE J. Sel. Areas Commun. , to appear, 2023
2023
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2023
Closest in time.
H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, M. Guizani, and D. I. Kim, “Rethinking wireless communication security in semantic internet of things,” IEEE Wireless Commun. Mag. , to appear, 2023
2023
Closest in time.
Y. Lin, Z. Gao, Y. Tu, H. Du, D. Niyato, J. Kang, and H. Yang, “A Blockchain-based Semantic Exchange Framework for Web 3.0 toward Participatory Economy,” IEEE Commun Mag , 2023
2023
Closest in time.
Y. Lin, H. Du, D. Niyato, J. Nie, J. Zhang, Y. Cheng, and Z. Yang, “Blockchain-aided secure semantic communication for AI-generated content in metaverse,” IEEE Open J. Comput. Soc. , vol. 4, pp. 72–83, 2023
2023
Closest in time.
H. Du, J. Liu, D. Niyato, J. Kang, Z. Xiong, J. Zhang, and D. I. Kim, “Attention-aware resource allocation and QoE analysis for metaverse xURLLC services,” IEEE J. Sel. Areas Commun. , to appear, 2023
2023
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S. Huang, Z. Wang, P. Li, B. Jia, T. Liu, Y. Zhu, W. Liang, and S.-C. Zhu, “Diffusion-based generation, optimization, and planning in 3D scenes,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2023, pp. 16 750–16 761
2023
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——, “Denoising diffusion error correction codes,” in Proc. Int. Conf. Mach. Learn. , Jul. 2023
2023
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M. Kim, R. Fritschek, and R. F. Schaefer, “Learning end-to-end channel coding with diffusion models,” in International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding , 2023, pp. 1–6
2023
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2023
Closest in time.
W. Mao, K. Xiong, Y. Lu, P. Fan, and Z. Ding, “Energy consumption minimization in secure multi-antenna UAV-assisted MEC networks with channel uncertainty,” IEEE Trans. Wireless Commun. , pp. 1–1, 2023
2023
Closest in time.
Z. Wang, J. Zhang, H. Du, E. Wei, B. Ai, D. Niyato, and M. Debbah, “Extremely large-scale MIMO: Fundamentals, challenges, solutions, and future directions,” IEEE Wireless Commun. , 2023
2023
Closest in time.
Z. Wang, J. Zhang, H. Q. Ngo, B. Ai, and M. Debbah, “Uplink precoding design for cell-free massive MIMO with iteratively weighted MMSE,” IEEE Trans. Commun. , vol. 71, no. 3, pp. 1646–1664, Mar. 2023
2023
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W. K. New, K.-K. Wong, H. Xu, K.-F. Tong, and C.-B. Chae, “Fluid antenna system: New insights on outage probability and diversity gain,” IEEE Trans. Wireless Commun. , to appear, 2023
2023
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M. Khammassi, A. Kammoun, and M.-S. Alouini, “A new analytical approximation of the fluid antenna system channel,” IEEE Trans. Wireless Commun. , to appear, 2023
2023
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Y. Zhao, F. Zhou, L. Feng, W. Li, and P. Yu, “Madrl-based 3d deployment and user association of cooperative mmwave aerial base stations for capacity enhancement,” Chinese J. Electron. , vol. 32, no. 2, pp. 283–294, 2023
2023
Closest in time.
Y. Lin, Z. Gao, H. Du, J. Kang, D. Niyato, Q. Wang, J. Ruan, and S. Wan, “DRL-based adaptive sharding for blockchain-based federated learning,” IEEE Trans. Commun. , 2023
2023
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H. Cao, C. Tan, Z. Gao, G. Chen, P.-A. Heng, and S. Z. Li, “A survey on generative diffusion model,” IEEE Trans. Knowledge Data Eng. , to appear, 2024
2024
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2024
Closest in time.
H. Du, Z. Li, D. Niyato, J. Kang, Z. Xiong, H. Huang, and S. Mao, “Diffusion-based reinforcement learning for edge-enabled AI-generated content services,” IEEE Trans. Mobile Comput. , to appear, 2024
2024
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2024
Closest in time.
Z. Sun and Y. Yang, “Difusco: Graph-based diffusion solvers for combinatorial optimization,” Adv. Neural Inf. Process. Syst. , vol. 36, 2024
2024
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B. Zhang, W. Luo, and Z. Zhang, “Enhancing adversarial robustness via score-based optimization,” Adv. Neural Inf. Process. Syst. , vol. 36, 2024
2024
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G. Giannone, A. Srivastava, O. Winther, and F. Ahmed, “Aligning optimization trajectories with diffusion models for constrained design generation,” Adv. Neural Inf. Process. Syst. , vol. 36, 2024
2024
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H. Chen, C. Lu, Z. Wang, H. Su, and J. Zhu, “Score regularized policy optimization through diffusion behavior,” in Proc. Int. Conf. Learn. Represent. , 2024
2024
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S. Zhou, Y. Du, S. Zhang, M. Xu, Y. Shen, W. Xiao, D.-Y. Yeung, and C. Gan, “Adaptive online replanning with diffusion models,” Adv. Neural Inf. Process. Syst. , vol. 36, 2024
2024
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K. Xu, S. Lu, B. Huang, W. Wu, and Q. Liu, “Stage-by-stage wavelet optimization refinement diffusion model for sparse-view CT reconstruction,” IEEE Trans. Med. Imaging , to appear, 2024
2024
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Z. Jiang, Z. Zhou, L. Li, W. Chai, C.-Y. Yang, and J.-N. Hwang, “Back to optimization: Diffusion-based zero-shot 3D human pose estimation,” in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis. , 2024, pp. 6142–6152
2024
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Y. Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, and A. Jamalipour, “Deep generative model and its applications in efficient wireless network management: A tutorial and case study,” IEEE Wireless Commun. , to appear, 2024
2024
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J. Brehmer, J. Bose, P. De Haan, and T. Cohen, “EDGI: Equivariant diffusion for planning with embodied agents,” Proc. Adv. Neural Inf. Process. Syst. , vol. 36, 2024
2024
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N. Van Huynh, J. Wang, H. Du, D. T. Hoang, D. Niyato, D. N. Nguyen, D. I. Kim, and K. B. Letaief, “Generative AI for physical layer communications: A survey,” IEEE Trans. on Cogn. Commun. Netw. , to appear, 2024
2024
Closest in time.
J. Wang, H. Du, D. Niyato, J. Kang, Z. Xiong, D. Rajan, S. Mao et al. , “A unified framework for guiding generative AI with wireless perception in resource constrained mobile edge networks,” IEEE Trans. Mobile Comput. , to appear, 2024
2024
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R. Zhang, K. Xiong, H. Du, D. Niyato, J. Kang, X. Shen, and H. V. Poor, “Generative AI-enabled vehicular networks: Fundamentals, framework, and case study,” IEEE Netw. , to appear, 2024
2024
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Q. Bao, Z. Hui, R. Zhu, P. Ren, X. Xie, and W. Yang, “Improving diffusion-based image restoration with error contraction and error correction,” in Proc. AAAI Conf. Artif. Intell. , vol. 38, no. 2, 2024, pp. 756–764
2024
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T. Wu, Z. Chen, D. He, L. Qian, Y. Xu, M. Tao, and W. Zhang, “CDDM: Channel denoising diffusion models for wireless semantic communications,” IEEE Trans. Wireless Commun. , to appear, 2024
2024
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Z. Wang, J. Zhang, H. Du, D. Niyato, S. Cui, B. Ai, M. Debbah, K. B. Letaief, and H. V. Poor, “A tutorial on extremely large-scale MIMO for 6G: Fundamentals, signal processing, and applications,” IEEE Commun. Surv. Tutor. , 2024
2024
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K. Cobbe, C. Hesse, J. Hilton, and J. Schulman, “Leveraging procedural generation to benchmark reinforcement learning,” in Proc. Int. Conf. Mach. Learn. , vol. 119. PMLR, 13–18 Jul 2020, pp. 2048–2056
2056
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