RNN Based Proactive Received Power Prediction Using Latest and Estimated Received Power

M. Sasaki, Naoki Shibuya, Kenichi Kawamura, Nobuaki Kuno, M. Inomata, W. Yamada, Takatsune Moriyama
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引用次数: 0

Abstract

We report a method for proactively predicting received power using GRU (Gated Recurrent Unit), which is one of RNN (Recurrent Neural Network) as deep learning. One of the input data of the GRU is the latest received power obtained at the receiver, and another is the pre-estimated received power estimated at the future target position of the receiver. The training and validation data use received power of Wi-Fi measured in an indoor environment. According to the prediction method using the proposed model, the root mean squared error for the validation data is about 1.0 dB for the median received power. The prediction accuracy was improved by 1.7 dB compared with the baseline which uses the latest observed values.
基于RNN的主动接收功率预测,利用最新接收功率和估计接收功率
我们报告了一种使用GRU(门控循环单元)主动预测接收功率的方法,GRU(门控循环单元)是RNN(循环神经网络)作为深度学习的一种。GRU的输入数据中,一个是在接收机处获得的最新接收功率,另一个是在接收机未来目标位置估计的预估接收功率。训练和验证数据使用在室内环境中测量的Wi-Fi接收功率。根据该模型的预测方法,验证数据的中位接收功率的均方根误差约为1.0 dB。与使用最新观测值的基线相比,预测精度提高了1.7 dB。
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