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A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders

A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders
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摘要 In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. . In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .
作者 Lusheng Xi Yanan Yu Jianzhong Yi Chao Dong Kai Niu Qiuping Huang Qiubin Gao Yongqiang Fei Lusheng Xi;Yanan Yu;Jianzhong Yi;Chao Dong;Kai Niu;Qiuping Huang;Qiubin Gao;Yongqiang Fei(Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China;CICT Mobile Communications Technology Co., Ltd., Beijing, China;State Key Laboratory of Wireless Mobile Communications, China Academy of Telecommunications Technology (CATT), Beijing, China)
出处 《Journal of Computer and Communications》 2023年第9期143-153,共11页 电脑和通信(英文)
关键词 Autoencoder Joint Training Separate Training CSI Feedback Autoencoder Joint Training Separate Training CSI Feedback
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