A scalable fingerprint-based angle-of-arrival machine learning approach for cellular mobile radio localization

2020 ◽  
Vol 157 ◽  
pp. 92-101
Author(s):  
Robson D.A. Timoteo ◽  
Daniel C. Cunha
Keyword(s):  
Machine Learning ◽  
Mobile Radio ◽  
Learning Approach ◽  
Angle Of Arrival ◽  
Machine Learning Approach ◽  
Radio Localization ◽  
Cellular Mobile Radio ◽  
Cellular Mobile

scholarly journals Doppler Spread Estimation Based on Machine Learning for an OFDM System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Eunchul Yoon ◽  
Soonbum Kwon ◽  
Unil Yun ◽  
Sun-Yong Kim
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
Training Data ◽  
Doppler Spread ◽  
Learning Approach ◽  
Ofdm System ◽  
Angle Of Arrival ◽  
Estimation Errors ◽  
Machine Learning Approach ◽  
Spread Estimation

In this paper, we propose a Doppler spread estimation approach based on machine learning for an OFDM system. We present a carefully designed neural network architecture to achieve good performance in a mixed-channel scenario in which channel characteristic variables such as Rician K factor, azimuth angle of arrival (AOA) width, mean direction of azimuth AOA, and channel estimation errors are randomly generated. When preprocessing the channel state information (CSI) collected under the mixed-channel scenario, we propose averaged power spectral density (PSD) sequence as high-quality training data in machine learning for Doppler spread estimation. We detail intermediate mathematical derivatives of the machine learning process, making it easy to graft the derived results into other wireless communication technologies. Through simulation, we show that the machine learning approach using the averaged PSD sequence as training data outperforms the other machine learning approach using the channel frequency response (CFR) sequence as training data and two other existing Doppler estimation approaches.

Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G

2018 ◽  
Vol 20 (2) ◽  
pp. 1124-1148 ◽  
Author(s):  
Jose A. del Peral-Rosado ◽  
Ronald Raulefs ◽  
Jose A. Lopez-Salcedo ◽  
Gonzalo Seco-Granados
Keyword(s):  
Mobile Radio ◽  
Radio Localization ◽  
Cellular Mobile Radio ◽  
Cellular Mobile

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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