长短期记忆网络(LSTM)是一种用于深度学习领域的人工回归神经网络(RNN)结构。与标准的前馈神经网络不同,LSTM具有反馈连接。它不仅可以处理单个数据点(如图像),还可以处理整个数据序列(如语音或视频)。例如,LSTM适用于未分段、连接的手写识别、语音识别、网络流量或IDSs(入侵检测系统)中的异常检测等任务。

最新论文

Channel estimation and signal detection are essential steps to ensure the quality of end-to-end communication in orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we develop a DDLSD approach, i.e., Data-driven Deep Learning for Signal Detection in OFDM systems. First, the OFDM system model is established. Then, the long short-term memory (LSTM) is introduced into the OFDM system model. Wireless channel data is generated through simulation, the preprocessed time series feature information is input into the LSTM to complete the offline training. Finally, the trained model is used for online recovery of transmitted signal. The difference between this scheme and existing OFDM receiver is that explicit estimated channel state information (CSI) is transformed into invisible estimated CSI, and the transmit symbol is directly restored. Simulation results show that the DDLSD scheme outperforms the existing traditional methods in terms of improving channel estimation and signal detection performance.

0
0
下载
预览
Top