论文标题
基于神经网络的范围与LTE通道脉冲脉冲响应在室内环境中进行本地化
Neural Network-Based Ranging with LTE Channel Impulse Response for Localization in Indoor Environments
论文作者
论文摘要
提出了通过细胞长期进化（LTE）信号基于神经网络（NN）的室内定位方法。该方法估计，从通道脉冲响应（CIR），是LTE EnodeB和接收器之间的范围。软件定义的无线电（SDR）提取了CIR，该CIR被馈送到长期的短期内存模型（LSTM）复发性神经网络（RNN）以估计范围。提出了实验结果，比较了所提出的方法与没有LSTM的基线RNN。结果表明，一个接收器在室内环境中导航100 m，同时接收一个LTE EnodeB的信号。在基线RNN中，范围均方平方误差（RMSE）分别从13.11 m和55.68 m降低至9.02 m和27.40 m。
A neural network (NN)-based approach for indoor localization via cellular long-term evolution (LTE) signals is proposed. The approach estimates, from the channel impulse response (CIR), the range between an LTE eNodeB and a receiver. A software-defined radio (SDR) extracts the CIR, which is fed to a long short-term memory model (LSTM) recurrent neural network (RNN) to estimate the range. Experimental results are presented comparing the proposed approach against a baseline RNN without LSTM. The results show a receiver navigating for 100 m in an indoor environment, while receiving signals from one LTE eNodeB. The ranging root-mean squared error (RMSE) and ranging maximum error along the receiver's trajectory were reduced from 13.11 m and 55.68 m, respectively, in the baseline RNN to 9.02 m and 27.40 m, respectively, with the proposed RNN-LSTM.
