Conditional Denoising Diffusion Probabilistic Models for Data
Reconstruction Enhancement in Wireless Communications

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Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications

IEEE Transactions on Machine Learning in Communications and Networking (IEEE TMLCN), 2023

30 October 2023

Matti Latva-aho

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Papers citing "Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications"

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Title
Conditional Denoising Diffusion Autoencoders for Wireless Semantic Communications Mehdi Letafati Samad Ali Matti Latva-aho DiffM 96 1 0 26 Sep 2025
Graph Signal Generative Diffusion Models Yigit Berkay Uslu Samar Hadou Sergio Rozada Shirin Saeedi Bidokhti Alejandro Ribeiro DiffM 133 0 0 21 Sep 2025
Diffusion Models for Future Networks and Communications: A Comprehensive Survey Nguyen Cong Luong Nguyen Duc Hai Duc Van Le H. T. Nguyen Thai-Hoc Vu ... Nguyen Duc Duy Anh Dusit Niyato Marco Di Renzo Dong In Kim Quoc-Viet Pham 165 1 0 03 Aug 2025
Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches Xiaozheng Gao Yichen Wang Bosen Liu Xiao Zhou Ruichen Zhang ... Dong In Kim Abbas Jamalipour Chau Yuen Jianping An Kai Yang 135 7 0 19 Jul 2025
Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication Chenguang Liu Jianjun Chen Yunfei Chen Yubei He Zhuangkun Wei Hongjian Sun Haiyan Lu Qi Hao 168 0 0 06 May 2025
FUSE: Fast Unified Simulation and Estimation for PDEsNeural Information Processing Systems (NeurIPS), 2024 Levi E. Lingsch Dana Grund Siddhartha Mishra Georgios Kissas AI4CE 278 4 0 23 May 2024