Analytical Approximation-Based Machine Learning Methods for User Positioning in Distributed Massive MIMO

Prasad, K. N. R. Surya Vara; Hossain, Ekram; Bhargava, V.K.; Mallick, Shankhanaad

doi:10.1109/access.2018.2805841

Cited by 17 publications

(22 citation statements)

References 34 publications

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“…But, these methods are not applicable in DM-MIMO systems, where single-antenna remote radio heads are considered. In [17] and [18], the Gaussian process regression (GPR) ML algorithm is employed based on RSS measurements in DM-MIMO systems. In [3], the performance of several ML algorithms, which are used in conjunction with fingerprint-based MT localization for DM-MIMO wireless systems configurations, is investigated and evaluated.…”

Section: User Positioning In Massive Mimo Systemmentioning

confidence: 99%

An Accurate, Robust and Low Dimensionality Deep Learning Localization Approach in DM-MIMO Systems Based on RSS

Fortier

2021

Preprint

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Currently, localization in distributed massive MIMO (DM-MIMO) systems based on the fingerprinting (FP) approach has attracted great interest. However, this method suffers from severe multipath and signal degradation such that its accuracy is deteriorated in complex propagation environments, which results in variable received signal strength (RSS). Therefore, providing robust and accurate localization is the goal of this work. In this paper, we propose an FP-based approach to improve the accuracy of localization by reducing the noise and the dimensions of the RSS data. In the proposed approach, the fingerprints rely solely on the RSS from the single-antenna MT collected at each of the receive antenna elements of the massive MIMO base station. After creating a radio map, principal component analysis (PCA) is performed to reduce the noise and redundancy. PCA reduces the data dimension which leads to the selection of the appropriate antennas and reduces complexity. A clustering algorithm based on K-means and affinity propagation clustering (APC) is employed to divide the whole area into several regions which improves positioning precision and reduces complexity and latency. Finally, in order to have high precise localization estimation, all similar data in each cluster are modeled using a well-designed deep neural network (DNN) regression. Simulation results show that the proposed scheme improves positioning accuracy significantly. This approach has high coverage and improves average root-mean-squared error (RMSE) performance to a few meters, which is expected in 5G and beyond networks. Consequently, it also proves the superiority of the proposed method over the previous location estimation schemes.

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Section: User Positioning In Massive Mimo Systemmentioning

confidence: 99%

An Accurate, Robust and Low Dimensionality Deep Learning Localization Approach in DM-MIMO Systems Based on RSS

Fortier

2021

Preprint

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“…There is much work that uses the RSS [ 20 , 21 , 22 , 23 , 24 ], the ToA [ 25 , 26 , 27 ], or their combinations [ 28 ]. Some use machine learning (ML) based schemes such as neural networks with a single hidden layer [ 21 , 26 , 28 ], variants of neural networks (i.e., deep belief networks [ 22 , 29 ], deep neural networks [ 30 , 31 ], fuzzy neural networks [ 32 ], artificial synaptic networks [ 25 ]), Gaussian regression [ 33 ], support vector machines (SVM) [ 27 ], random decision forest [ 34 ], or combinations of them [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to Position Estimation from Channel Impulse Responses

Niitsoo

Edelhäußer

Eberlein

et al. 2019

Sensors

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Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.

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“…with y ms being the actual value of the mth output attribute of the sth sample [38]. The smaller γ is, the closer the output of neural network is to the actual value.…”

Section: Structure Determinationmentioning

confidence: 99%

Weight and Structure Determination Neural Network Aided With Double Pseudoinversion for Diagnosis of Flat Foot

Chen

Huang

et al. 2019

IEEE Access

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Deep learning models often have complicated structures with low computational speed and the requirement of a large amount of storage space, which limits their own practical application on some devices with insufficient computing power. This paper proposes the weight and structure determination neural network aided with double pseudoinversion (WASDNN-DP) that can overcome these shortcomings. First, the model structure, theoretical bases, and the algorithms of WASDNN-DP are given. In the process of constructing the network, the weight matrix between the hidden layer and the output layer is first randomly generated. After the weight matrix between the input layer and the hidden layer is analytically determined, the weight matrix between the hidden layer and the output layer is re-determined by the pseudo-inverse method. Furthermore, in WASDNN-DP, the structure of the neural network is determined by a progressive method. Subsequently, based on two datasets collected from children aged 7-15 years by using smart insoles, the comparative experiments between WASDNN-DP and some other machine learning models are carried out, which illustrate the superiority of the proposed WASDNN-DP in the diagnosis of flat foot. INDEX TERMS Classification algorithms, feedforward neural networks, weight and structure determination (WASD), computer aided diagnosis.

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Analytical Approximation-Based Machine Learning Methods for User Positioning in Distributed Massive MIMO

Cited by 17 publications

References 34 publications

An Accurate, Robust and Low Dimensionality Deep Learning Localization Approach in DM-MIMO Systems Based on RSS

An Accurate, Robust and Low Dimensionality Deep Learning Localization Approach in DM-MIMO Systems Based on RSS

A Deep Learning Approach to Position Estimation from Channel Impulse Responses

Weight and Structure Determination Neural Network Aided With Double Pseudoinversion for Diagnosis of Flat Foot

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