NDR: Noise and Dimensionality Reduction of CSI for Indoor Positioning Using Deep Learning

Sobehy, Abdallah; Renault, Éric; Mühlethaler, Paul

doi:10.1109/globecom38437.2019.9013195

Cited by 10 publications

(22 citation statements)

References 14 publications

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“…With a careful inspection, it can be notices that the magnitude component shows the highest stability. This conclusion is further supported by the analysis performed in [11,12]. Consequently, we chose the magnitude to be the input component to the deep learning model.…”

Section: Learning Model Inputmentioning

confidence: 60%

“…Since this process has to be performed for each of the 15k training samples multiplied by the 16 antennas, it has to be relatively fast. This process is achieved by polynomial fitting on four sections over the subcarrier spectrum [11]. Figure 3 shows the four batches, each with a different color, and the degree of the polynomial used to fit the line.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Dividing the spectrum into four sections increases the accuracy of the overall fitting. More details of the fitting process can be found in [11]. The following steps is to use a reduced number of points along the fitted lines instead of the whole set of 924 points.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…In addition, the reduction allows to build a more complex MLP that trains in less time. In [11], 66 points are used to represent the whole spectrum. We chose the input feature to be the difference (slope) between two consecutive points.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Figure 1 shows the experimental setup as well as a sketch of the MIMO antenna and the position of its center. Another solution that is tested on the same data set is NDR [11] which is based on the magnitude component of the CSI. First, the magnitude values are preprocessed by fitting a line through the points.…”

Section: Introductionmentioning

confidence: 99%

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CSI Based Indoor Localization Using Ensemble Neural Networks

Sobehy

Renault

Mühlethaler

2020

Machine Learning for Networking

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Indoor localization has attracted much attention due to its indispensable applications e.g. autonomous driving, Internet-Of-Things (IOT), and routing, etc. Received Signal Strength Indicator (RSSI) was used intensively to achieve localization. However, due to its temporal instability, the focus has shifted towards the use of Channel State Information (CSI) aka channel response. In this paper, we propose a deep learning solution for the indoor localization problem using the CSI of a 2 × 8 Multiple Input Multiple Output (MIMO) antenna. The variation of the magnitude component of the CSI is chosen as the input for a multilayer Perceptron (MLP) neural network. Data augmentation is used to improve the learning process. Finally, various MLP neural networks are constructed using different portions of the training set and different hyperparameters. An ensemble neural network technique is then used to process the predictions of the MLPs in order to enhance the position estimation. Our method is compared with two other deep learning solutions; one that uses Convolutional Neural Network (CNN), and the other uses MLP. The proposed method yields higher accuracy than its counterparts.

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Section: Learning Model Inputmentioning

confidence: 60%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CSI Based Indoor Localization Using Ensemble Neural Networks

Sobehy

Renault

Mühlethaler

2020

Machine Learning for Networking

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Generalization aspect of accurate machine learning models for CSI-based localization

2021

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Localization is the process of determining the position of an entity in a given coordinate system. Due to its wide range of applications (e.g. autonomous driving, Internet-of-Things), it has gained much focus from the industry and academia. Channel State Information (CSI) has overtaken Received Signal Strength Indicator (RSSI) to achieve localization given its temporal stability and rich information. In this paper, we extend our previous work by combining classical and deep learning methods in an attempt to improve the localization accuracy using CSI. We then test the generalization aspect of both approaches in different environments by splitting the training and test sets such that their intersection is reduced when compared with uniform random splitting. The deep learning approach is a Multi Layer Perceptron Neural Network (MLP NN) and the classical machine learning method is based on K-nearest neighbors (KNN). The estimation results of both approaches outperform state-of-the-art performance on the same dataset. We illustrate that while the accuracy of both approaches deteriorates when tested for generalization, deep learning exhibits a higher potential to perform better beyond the training set. This conclusion supports recent state-of-the-art attempts to understand the behaviour of deep learning models.

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Implementation and Evaluation of Novel Architecture Using Optical Wireless for WLAN Control Plane

Shiina¹,

Tamaki

Hara

et al. 2021

IEEE Access

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We propose a novel wireless control system architecture that divides the wireless service area into smaller optical cells and centrally controls the user equipment (UE) connections under each optical cell. The proposal transmits via IoT smart lighting an optical identifier (ID) specifying connection information to the UE with illuminance sensor. The received optical ID indicates the optimum connection destination. Two solutions are proposed to overcome the hardware restrictions faced by optical ID transmission/reception. Oversampled edge-excluded receiving scheme (OE) reduces the error rate; the signal is oversampled and each window subjected to majority decision. Optical ID reception with lower error rate than conventional approach is realized with a minimum received illuminance of just 8.5 lx (background illuminance 226.67 lx) and a modulation factor of 7 %. Fast optical ID authentication (FA) reduces the authentication cycle preceding optical ID reception by performing correlation calculation between the cyclic matrix and an optical ID list. FA shortens the authentication cycle by 62.5 % (1200 ms to 450 ms). Furthermore, an optical cell control algorithm (ScanLine-based/MinDist-based) is proposed to offset the deterioration in network quality created by non-uniform UE distribution. ScanLine-based algorithm controls optical cells in scanning line manner, while MinDist-based favors optical cells with the minimum distance from the access point (AP). It is confirmed that with an optical cell radius of 5 m or less, the capacity difference per user is reduced compared to the existing RSSI-based alternative while reducing the deterioration of total capacity in extremely biased user distribution scenarios.

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NDR: Noise and Dimensionality Reduction of CSI for Indoor Positioning Using Deep Learning

Cited by 10 publications

References 14 publications

CSI Based Indoor Localization Using Ensemble Neural Networks

CSI Based Indoor Localization Using Ensemble Neural Networks

Generalization aspect of accurate machine learning models for CSI-based localization

Implementation and Evaluation of Novel Architecture Using Optical Wireless for WLAN Control Plane

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