新型的基于堆栈式ELM的时变信道预测方法

针对高速移动场景正交频分复用(orthogonal frequency division multiplexing,OFDM)系统,提出了一种新的基于堆栈式极限学习机(extreme learning machine,ELM)的时变信道预测方法.为了捕获输入数据的深层信息,基于单隐藏层神经网络,首先利用堆栈式ELM方法...

Novel time-varying channel prediction method based on stacked ELM

Aiming at the orthogonal frequency division multiplexing(OFDM)system under high-speed mobile scenario,a novel stacked extreme learning machine(ELM)based time-varying channel prediction method is proposed.Based on the single hidden layer neural network,to capture the deep information of the input data,the ELM method is firstly used to extract the deep features from the historical channel and obtain the initial output weight of the network.Then,to adapt to the channel changes,the proposed method updates the output weights of the network in real time based on the newly constructed historical channel samples and the initial output weights,and obtains the channel at the current moment based on the updated output weights.Finally,the simulation results shav that compared with the existing schemes,the proposed method has high prediction accuracy and is suitable for high-speed mobile scenarios.