Department of Electronic Engineering, Myongji University, Yongin 449-728, Korea.
Intel Labs, Intel Corporation, Santa Clara, CA 95054, USA.
Sensors (Basel). 2018 Nov 20;18(11):4057. doi: 10.3390/s18114057.
Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model.
识别信道状态是视距还是非视距有助于优化无线通信中的基于位置的服务。接收信号强度识别和信道状态信息用于估计室内无线局域网中正交频分复用系统的信道条件。本文提出了一种联合卷积神经网络和递归神经网络架构来对信道条件进行分类。卷积神经网络从信道状态信息数据的频域特征中提取特征，递归神经网络从接收信号强度识别和数据包传输之间的信道状态信息的时变特征中提取特征。在室内传播环境下验证了所提出方法的性能。实验结果表明，与传统的递归神经网络模型相比，所提出的方法在分类性能上提高了 2%。
