Time-Gating Technique to Emulate New Scenarios

Ramírez-Arroyo, Alejandro; Alex‐Amor, Antonio; Garcia-Garcia, Carmelo; Palomares‐Caballero, Ángel; Padilla, Pablo; Valenzuela-Valdés, Juan F.

doi:10.23919/eucap51087.2021.9411187

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…In our previous works [29], [30], [53]- [55], we have applied post-processing techniques in order to modify several signals in anechoic and reverberation chambers. For example, a time-domain signal acquired in the semi-anechoic and semireverberation chamber can be modified to emulate several environments [29], [30]. These works illustrate that it is possible to recreate different types of communication scenarios from measurements acquired in the chambers.…”

Section: Generation Of Scenariosmentioning

confidence: 99%

Artificial Intelligence and Dimensionality Reduction: Tools for Approaching Future Communications

Ramírez-Arroyo

García

Alex‐Amor

et al. 2022

IEEE Open J. Commun. Soc.

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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.

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Section: Generation Of Scenariosmentioning

confidence: 99%

Artificial Intelligence and Dimensionality Reduction: Tools for Approaching Future Communications

Ramírez-Arroyo

García

Alex‐Amor

et al. 2022

IEEE Open J. Commun. Soc.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In our previous works [28], [29], [45]- [47], we have applied post-processing techniques in order to modify several signals in anechoic and reverberation chambers. For example, a time-domain signal acquired in the semi-anechoic and semireverberation chamber can be modified to emulate several environments [28], [29]. These works illustrate that is possible to recreate different types of communication scenarios from measurements acquired in the chambers.…”

Section: Generation Of Scenariosmentioning

confidence: 99%

Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications

Ramírez-Arroyo,

García,

Alex-Amor

et al. 2021

Preprint

Self Cite

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This article presents a novel application of the tdistributed Stochastic Neighbor Embedding (t-SNE) clustering algorithm to the telecommunication field. t-SNE is a dimensionality reduction (DR) 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 (VAE) 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.

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“…I MPROVING the performance of wireless links requires a proper characterization and knowledge of multiple channel parameters: direction of arrival (DoA), time of arrival (ToA), delay spread, path loss, and K factor, among others [1], [2]. With knowledge of the channel parameters, different scenarios can be effectively distinguished [3], even recreated and emulated through the use of post-processing techniques based on the creation/removal of reflections in the communication channel [4], [5]. This is fundamental in today's interconnected society, considering the huge variety of propagation environments associated with different communication scenarios: 5G/6G mobile [6], [7], RIS-aided [8], [9], industrial [10], [11], and vehicle-to-everything [12]- [16] networks.…”

Section: Introductionmentioning

confidence: 99%

Joint Ultra-Wideband Characterization of Azimuth, Elevation, and Time of Arrival With Toric Arrays

Ramírez-Arroyo,

Alex-Amor,

Medina

et al. 2024

IEEE Trans. Wireless Commun.

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In this paper, we present an analytical framework for the joint characterization of the 3D direction of arrival (DoA), i.e., azimuth and elevation components, and time of arrival (ToA) in multipath environments. The analytical framework is based on the use of nearly frequency-invariant beamformers (FIB) formed by toric arrays. The frequency response of the toric array is expanded as a series of phase modes, which leads to azimuth-time and elevation-time diagrams from which the 3D DoA and the ToA of the incoming waves can be extracted over a wide bandwidth. Firstly, we discuss some practical considerations, advantages and limitations of using the analytical method. Subsequently, we perform a parametric study to analyze the influence of the method parameters on the quality of the estimation. The method is tested in single-path and multipath mm-wave environments over a large bandwidth. The results show that the proposed method improves the quality of the estimation, i.e., decreases the level of the artifacts, compared to other state-of-art FIB approaches based on the use of single/concentric circular and elliptical arrays.

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Time-Gating Technique to Emulate New Scenarios

Cited by 5 publications

References 18 publications

Artificial Intelligence and Dimensionality Reduction: Tools for Approaching Future Communications

Artificial Intelligence and Dimensionality Reduction: Tools for Approaching Future Communications

Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications

Joint Ultra-Wideband Characterization of Azimuth, Elevation, and Time of Arrival With Toric Arrays

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