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基于深度学习的信道估计 被引量:3

Channel estimation based on deep learning
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摘要 目前深度学习方法在物理层通信中得到了广泛的应用,信道估计是物理层通信中的关键部分,与传统信道估计算法相比,深度学习方法在信道估计方面具有一定的优势。本研究介绍了深度学习中常见的神经网络模型,从模型驱动和数据驱动方式两个方面分别阐述了深度学习方法在信道估计中的应用。综述了将深度学习方法应用在信道估计中的最新研究进展。为了满足下一代移动通信的性能需求,讨论了智能物理层通信中信道估计面临的挑战与机遇。 Deep learning has been widely used in physical layer communication.Channel estimation is a key part of physical layer communication.Compared with traditional channel estimation algorithms,deep learning possesses some advantages in channel estimation.The common neural network models in deep learning was introduced,and the application of deep learning in channel estimation was explained from two aspects of model-driven and data-driven methods.The latest research progress in deep was learning applying to channel estimation has been reviewed.To meet the performance requirements of next-generation mobile communications,the challenges and opportunities of channel estimation in intelligent physical layer communications have been discussed.
作者 石佳琪 金桂月 金基宇 樊磊 SHI Jiaqi;JIN Guiyue;JIN Jiyu;FAN Lei(School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China)
出处 《大连工业大学学报》 CAS 北大核心 2021年第5期367-376,共10页 Journal of Dalian Polytechnic University
基金 辽宁省自然科学基金项目(2019-ZD-0294).
关键词 深度学习 信道估计 智能物理层通信 6G deep learning channel estimation intelligent physical layer communication 6G
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  • 1Xiaohu YOU,Cheng-Xiang WANG,Jie HUANG,Xiqi GAO,Zaichen ZHANG,Mao WANG,Yongming HUANG,Chuan ZHANG,Yanxiang JIANG,Jiaheng WANG,Min ZHU,Bin SHENG,Dongming WANG,Zhiwen PAN,Pengcheng ZHU,Yang YANG,Zening LIU,Ping ZHANG,Xiaofeng TAO,Shaoqian LI,Zhi CHEN,Xinying MA,Chih-Lin I,Shuangfeng HAN,Ke LI,Chengkang PAN,Zhimin ZHENG,Lajos HANZO,Xuemin(Sherman)SHEN,Yingjie Jay GUO,Zhiguo DING,Harald HAAS,Wen TONG,Peiying ZHU,Ganghua YANG,Jun WANG,Erik GLARSSON,Hien Quoc NGO,Wei HONG,Haiming WANG,Debin HOU,Jixin CHEN,Zhe CHEN,Zhangcheng HAO,Geoffrey Ye LI,Rahim TAFAZOLLI,Yue GAO,HVincent POOR,Gerhard P.FETTWEIS,Ying-Chang LIANG.Towards 6G wireless communication networks:vision,enabling technologies,and new paradigm shifts[J].Science China(Information Sciences),2021,64(1):1-74. 被引量:228

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