MaMIMO CSI-Based Positioning using CNNs: Peeking inside the Black Box

Bast, Sibren De; Pollin, Sofie

doi:10.1109/iccworkshops49005.2020.9145412

Cited by 23 publications

(12 citation statements)

References 6 publications

(9 reference statements)

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“…Finally, we will show the flexibility of our IMU-aided tracking system in dealing with IMU measurements with varying precision. [41], [42]. The dataset contains CSI and position tags of a massive MIMO-OFDM system measured by the National Instruments 5G Massive MIMO testbed 2 .…”

Section: Methodsmentioning

confidence: 99%

CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

Zhang¹,

Sifaou²,

Li³

2022

Preprint

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Deep learning has been widely adopted for channel state information (CSI)-fingerprinting indoor localization systems. These systems usually consist of two main parts, i.e., a positioning network that learns the mapping from high-dimensional CSI to physical locations and a tracking system that utilizes historical CSI to reduce the positioning error. This paper presents a new localization system with high accuracy and generality. On the one hand, the receptive field of the existing convolutional neural network (CNN)-based positioning networks is limited, restricting their performance as useful information in CSI is not explored thoroughly. As a solution, we propose a novel attention-augmented Residual CNN to utilize the local information and global context in CSI exhaustively. On the other hand, considering the generality of a tracking system, we decouple the tracking system from the CSI environments so that one tracking system for all environments becomes possible. Specifically, we remodel the tracking problem as a denoising task and solve it with deep trajectory prior. Furthermore, we investigate how the precision difference of inertial measurement units will adversely affect the tracking performance and adopt plug-and-play to solve the precision difference problem. Experiments show the superiority of our methods over existing approaches in performance and generality improvement.

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Section: Methodsmentioning

confidence: 99%

CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

Zhang¹,

Sifaou²,

Li³

2022

Preprint

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“…In another work of [ 178 ], deep NN was used in the development of a robust and accurate localization scheme for a distributed massive MIMO system. CSI-based positioning is discussed in [ 179 ] using CNNs a black box and experimented on the opening of the black box using. The authors have also discussed the advantages and disadvantages of the use of an open dataset collected in a real scenario 64-antenna Ma-MIMO system.…”

Section: Rl and DL Application In Mimomentioning

confidence: 99%

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

Naeem

Pietro

Coronato

2021

Sensors

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The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

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“…We are interested in evaluating the ability of the deep learning model to generalize which can give it a decisive edge over the KNN method despite the higher error localization error. An impressive indoor localization accuracy has been achieved using a larger MIMO antenna (8 × 8) and a very deep Convolutional Neural Network (CNN) [ 13 ]. The proposed CNN architecture is based on the DenseNet architecture [ 14 ] which was originally created for the well-known image recognition competition ImageNet [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

“…The DenseNet [ 14 ] proved itself as one of the best solutions in terms of classification accuracy. The CNN-based solution [ 13 ] is tested on a dataset different from the dataset on which our methods are tested. However, we are not aware of a CSI-based indoor localization solution that is able to achieve an error lower than the 17 mm error of [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

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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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MaMIMO CSI-Based Positioning using CNNs: Peeking inside the Black Box

Cited by 23 publications

References 6 publications

CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

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

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