@misc{10481/74131,
year = {2022},
month = {3},
url = {http://hdl.handle.net/10481/74131},
abstract = {This article presents a novel application of the t-distributed Stochastic Neighbor Embedding
(t-SNE) clustering algorithm to the telecommunication field. t-SNE is a dimensionality reduction algorithm
that allows the visualization of large dataset into a 2D plot. We present the applicability of this algorithm
in a communication channel dataset formed by several scenarios (anechoic, reverberation, indoor and
outdoor), and by using six channel features. Applying this artificial intelligence (AI) technique, we are
able to separate different environments into several clusters allowing a clear visualization of the scenarios.
Throughout the article, it is proved that t-SNE has the ability to cluster into several subclasses, obtaining
internal classifications within the scenarios themselves. t-SNE comparison with different dimensionality
reduction techniques (PCA, Isomap) is also provided throughout the paper. Furthermore, post-processing
techniques are used to modify communication scenarios, recreating a real communication scenario from
measurements acquired in an anechoic chamber. The dimensionality reduction and classification by using
t-SNE and Variational AutoEncoders show good performance distinguishing between the recreation and
the real communication scenario. The combination of these two techniques opens up the possibility for
new scenario recreations for future mobile communications. This work shows the potential of AI as a
powerful tool for clustering, classification and generation of new 5G propagation scenarios.},
organization = {Spanish Program of Research, Development, and Innovation under Project RTI2018-102002-A-I00},
organization = {Junta de Andalucía under Project B-TIC-402-UGR18 and Project P18.RT.4830},
organization = {Ministerio de Universidades, Gobierno de España under Predoctoral Grant FPU19/01251},
publisher = {IEEE},
keywords = {Artificial intelligence},
keywords = {Clustering},
keywords = {Dimensionality reduction},
keywords = {Propagation},
keywords = {t-SNE},
keywords = {Unsupervised learning},
keywords = {Wireless communications},
title = {Artificial Intelligence and Dimensionality Reduction: Tools for Approaching Future Communications},
doi = {10.1109/OJCOMS.2022.3156473},
author = {Ramírez Arroyo, Alejandro and García Martínez, María Luz and Alex Amor, Antonio and Valenzuela Valdes, Juan Francisco},
}