With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

中文翻译：

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使用残差学习和信道状态信息的室内定位系统

随着基于位置的服务需求的增长，基于神经网络的智能室内定位技术由于其较高的定位精度而引起了人们的极大兴趣。但是，深层神经网络通常会受到降级和梯度消失的影响。为了填补这一空白，我们提出了一种新颖的室内定位系统，包括去噪神经网络和残差网络（ResNet），以通过信道状态信息（CSI）预测移动物体的位置。在ResNet中，为防止过度拟合，我们将所有残差块替换为随机残差块。特别是，我们探索了远程随机快捷连接（LRSSC），以解决降级问题和梯度消失。为了获得大的接收场而又不丢失信息，我们利用ResNet后面的膨胀卷积。