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  • 澳门银河赌钱APP下载 G. Y. Li and B. Juang
  • 日期:2019-08-04   点击:   作者:澳门银河网站app   来源:澳门银河会员   字体:[ ]

To recreate the simulation results, 澳门银河会员, 这是一个利用深度进修东西箱中的是非期存储器(LSTM)网络在OFDM系统信号检测吸收器上实现标记分类的例子, This is an example of using the long short-term memory (LSTM) network in the Deep Learning Toolbox to achieve symbol classification at the receiver for signal detection in OFDM systems. 基于LSTM的神经网络是针对单个子载波举办练习的 , To test the robustness of the neural network, Feb. 2018. , Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems ,并与最小二乘(LS)和最小均方误差(MMSE)预计举办了较量,对每一个发送的OFDM包应用随机相移 。

该神经网络计较标记误码率(SER), G. Y. Li and B. Juang。

in IEEE Wireless Communications Letters, The wireless channel is assumed to be fixed during the offline training and the online deployment stages in this initial investigation. 为了测试神经网络的鲁棒性, where the symbol error rate (SER) is calculated and compared with the least square (LS) and minimum mean square error (MMSE) estimations. 在劈头研究的离线练习和在线陈设阶段, 假设无线信道是牢靠稳定的 , pp. 114-117,。

The impacts of the number of pilot symbols and the length of the cyclic prefix (CP) are considered. 要从头建设仿真模仿功效, please load the corresponding mat file and run the script Testing.m. 参考文献: H. Ye, vol. 7, no. 1,请加载相应的mat文件并运行Testing.m, 澳门银河会员, The LSTM-based neural network is trained for a single subcarrier。

a random phase shift is applied for each transmitted OFDM packet. 思量了导频标记个数和轮回前缀长度的影响。