scholarly journals Channel state information–based multi-level fingerprinting for indoor localization with deep learning

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880671 ◽  
Author(s):  
Tao Li ◽  
Hai Wang ◽  
Yuan Shao ◽  
Qiang Niu
Keyword(s):  
Deep Learning ◽  
Channel State Information ◽  
Deep Neural Networks ◽  
Training Phase ◽  
Channel State ◽  
Positioning Accuracy ◽  
State Information ◽  
Filtering Method ◽  
Multi Level ◽  
Better Than

With the rapid growth of indoor positioning requirements without equipment and the convenience of channel state information acquisition, the research on indoor fingerprint positioning based on channel state information is increasingly valued. In this article, a multi-level fingerprinting approach is proposed, which is composed of two-level methods: the first layer is achieved by deep learning and the second layer is implemented by the optimal subcarriers filtering method. This method using channel state information is termed multi-level fingerprinting with deep learning. Deep neural networks are applied in the deep learning of the first layer of multi-level fingerprinting with deep learning, which includes two phases: an offline training phase and an online localization phase. In the offline training phase, deep neural networks are used to train the optimal weights. In the online localization phase, the top five closest positions to the location position are obtained through forward propagation. The second layer optimizes the results of the first layer through the optimal subcarriers filtering method. Under the accuracy of 0.6 m, the positioning accuracy of two common environments has reached, respectively, 96% and 93.9%. The evaluation results show that the positioning accuracy of this method is better than the method based on received signal strength, and it is better than the support vector machine method, which is also slightly improved compared with the deep learning method.

scholarly journals CC-DTW: An Accurate Indoor Fingerprinting Localization Using Calibrated Channel State Information and Modified Dynamic Time Warping

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1984 ◽  
Author(s):  
Zhongliang Deng ◽  
Xiao Fu ◽  
Qianqian Cheng ◽  
Lingjie Shi ◽  
Wen Liu
Keyword(s):  
Channel State Information ◽  
Dynamic Time Warping ◽  
Local Area Network ◽  
Location Based Services ◽  
Area Network ◽  
Time Warping ◽  
Channel State ◽  
Positioning Accuracy ◽  
State Information ◽  
Dynamic Time

Indoor wireless local area network (WLAN) based positioning technologies have boomed recently because of the huge demands of indoor location-based services (ILBS) and the wide deployment of commercial Wi-Fi devices. Channel state information (CSI) extracted from Wi-Fi signals could be calibrated and utilized as a fine-grained positioning feature for indoor fingerprinting localization. One of the main factors that would restrict the positioning accuracy of fingerprinting systems is the spatial resolution of fingerprints (SRF). This paper mainly focuses on the improvement of SRF for indoor CSI-based positioning and a calibrated CSI feature (CCF) with high SRF is established based on the preprocess of both measured amplitude and phase. In addition, a similarity calculation metric for the proposed CCF is designed based on modified dynamic time warping (MDTW). An indoor fingerprinting method based on CCF and MDTW, named CC-DTW, is then proposed to improve the positioning accuracy in indoors. Experiments are conducted in two indoor office testbeds, and the performances of the proposed CC-DTW, one time-reversal (TR) based approach and one Euclidean distance (ED) based approach are evaluated and discussed. The results show that the SRF of CC-DTW outperforms the TR-based one and the ED-based one in both two testbeds in terms of the receiver operating characteristic (ROC) curve metric, and the area under curve (AUC) metric.

scholarly journals Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach

2021 ◽  
Vol 7 ◽  
pp. e682
Author(s):  
Mohamed Hassan Essai Ali ◽  
Ibrahim B.M. Taha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Channel State Information ◽  
Short Term Memory ◽  
Superior Performance ◽  
Short Term ◽  
Channel State ◽  
State Information ◽  
Term Memory ◽  
Long Short Term Memory

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.

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