CN110445581A - 基于卷积神经网络降低信道译码误码率的方法 - Google Patents
基于卷积神经网络降低信道译码误码率的方法 Download PDFInfo
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Abstract
Description
层数 | 层1 | 层2 | 层3 | 层4 | 层5 |
层类型 | 输入 | 一维卷积 | 一维卷积 | 一维卷积 | 输出 |
卷积核参数 | 9 | 3 | 3 | 15 | / |
感受野参数 | 64 | 32 | 16 | 1 | / |
激活函数 | ReLU | ReLU | ReLU | Linear | / |
超参数类型 | 差参数设置 |
学习率 | 0.001 |
训练周期 | 1000 |
批次训练数据 | 700 |
初始化方法 | Xavier |
优化器 | Adam |
损失函数 | MSE |
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Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111224905A (zh) * | 2019-12-25 | 2020-06-02 | 西安交通大学 | 一种大规模物联网中基于卷积残差网络的多用户检测方法 |
CN112382332A (zh) * | 2020-11-20 | 2021-02-19 | 广东工业大学 | 一种用于nand闪存芯片信号检测的方法及装置 |
CN112464483A (zh) * | 2020-12-04 | 2021-03-09 | 核工业二一六大队 | 一种基于遗传神经网络算法的测井曲线重构方法 |
CN112803951A (zh) * | 2019-11-14 | 2021-05-14 | 北京大学 | 一种降低通信系统接收信号的噪声的方法及接收端和通信系统 |
CN113271123A (zh) * | 2021-04-27 | 2021-08-17 | 西安电子科技大学广州研究院 | 一种新型计算信道解码的llr近似值的方法和系统 |
CN114337884A (zh) * | 2022-01-06 | 2022-04-12 | 兰州大学 | 基于深度学习的相位噪声补偿和信道译码联合设计方法 |
CN116264704A (zh) * | 2023-05-08 | 2023-06-16 | 深圳大学 | 基于信道感知和强化学习的低功耗广域网通感融合方法 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020034161A1 (en) * | 2000-05-31 | 2002-03-21 | Luc Deneire | Method and apparatus for channel estimation |
CN108833313A (zh) * | 2018-07-12 | 2018-11-16 | 北京邮电大学 | 一种基于卷积神经网络的无线信道估计方法及装置 |
CN109428673A (zh) * | 2017-08-28 | 2019-03-05 | 中国科学技术大学 | 用于解码信号的方法、设备以及存储设备 |
CN109450830A (zh) * | 2018-12-26 | 2019-03-08 | 重庆大学 | 一种高速移动环境下基于深度学习的信道估计方法 |
CN109462457A (zh) * | 2019-01-05 | 2019-03-12 | 苏州怡林城信息科技有限公司 | 一种Polar码译码方法、译码装置和译码器 |
CN109756432A (zh) * | 2017-11-01 | 2019-05-14 | 展讯通信(上海)有限公司 | Ofdm信道估计方法和装置 |
-
2019
- 2019-08-10 CN CN201910736687.8A patent/CN110445581B/zh active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020034161A1 (en) * | 2000-05-31 | 2002-03-21 | Luc Deneire | Method and apparatus for channel estimation |
CN109428673A (zh) * | 2017-08-28 | 2019-03-05 | 中国科学技术大学 | 用于解码信号的方法、设备以及存储设备 |
CN109756432A (zh) * | 2017-11-01 | 2019-05-14 | 展讯通信(上海)有限公司 | Ofdm信道估计方法和装置 |
CN108833313A (zh) * | 2018-07-12 | 2018-11-16 | 北京邮电大学 | 一种基于卷积神经网络的无线信道估计方法及装置 |
CN109450830A (zh) * | 2018-12-26 | 2019-03-08 | 重庆大学 | 一种高速移动环境下基于深度学习的信道估计方法 |
CN109462457A (zh) * | 2019-01-05 | 2019-03-12 | 苏州怡林城信息科技有限公司 | 一种Polar码译码方法、译码装置和译码器 |
Non-Patent Citations (1)
Title |
---|
JUAN P. DOMINGUEZ-MORALES等: "Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach", 《2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)》 * |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112803951A (zh) * | 2019-11-14 | 2021-05-14 | 北京大学 | 一种降低通信系统接收信号的噪声的方法及接收端和通信系统 |
CN111224905A (zh) * | 2019-12-25 | 2020-06-02 | 西安交通大学 | 一种大规模物联网中基于卷积残差网络的多用户检测方法 |
CN112382332A (zh) * | 2020-11-20 | 2021-02-19 | 广东工业大学 | 一种用于nand闪存芯片信号检测的方法及装置 |
CN112382332B (zh) * | 2020-11-20 | 2024-02-23 | 广东工业大学 | 一种用于nand闪存芯片信号检测的方法及装置 |
CN112464483A (zh) * | 2020-12-04 | 2021-03-09 | 核工业二一六大队 | 一种基于遗传神经网络算法的测井曲线重构方法 |
CN112464483B (zh) * | 2020-12-04 | 2022-12-20 | 核工业二一六大队 | 一种基于遗传神经网络算法的测井曲线重构方法 |
CN113271123A (zh) * | 2021-04-27 | 2021-08-17 | 西安电子科技大学广州研究院 | 一种新型计算信道解码的llr近似值的方法和系统 |
CN113271123B (zh) * | 2021-04-27 | 2022-03-25 | 西安电子科技大学广州研究院 | 一种新型计算信道解码的llr近似值的方法和系统 |
CN114337884A (zh) * | 2022-01-06 | 2022-04-12 | 兰州大学 | 基于深度学习的相位噪声补偿和信道译码联合设计方法 |
CN114337884B (zh) * | 2022-01-06 | 2023-06-09 | 兰州大学 | 基于深度学习的相位噪声补偿和信道译码联合设计方法 |
CN116264704A (zh) * | 2023-05-08 | 2023-06-16 | 深圳大学 | 基于信道感知和强化学习的低功耗广域网通感融合方法 |
CN116264704B (zh) * | 2023-05-08 | 2023-09-08 | 深圳大学 | 基于信道感知和强化学习的低功耗广域网通感融合方法 |
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Inventor after: Li Jun Inventor after: Wei Kang Inventor after: Wang Cheng Inventor after: Zhao Xiwei Inventor after: Wu Pingyang Inventor after: Liu Qian Inventor after: Gui Linqing Inventor before: Zhao Xiwei Inventor before: Wu Pingyang Inventor before: Liu Qian Inventor before: Wang Cheng Inventor before: Li Jun Inventor before: Gui Linqing Inventor before: Wei Kang |