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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Datasets of Indoor Wireless Channel Measurements for Machine Learning Applications

Authors: Pastore, Adriano; Ghani, Armin;

Datasets of Indoor Wireless Channel Measurements for Machine Learning Applications

Abstract

This is a simple dataset of raw IQ measurements on a point-to-point wireless indoor channel, captured in a static laboratory environment. A sequence of random symbols (either QPSK symbols or random Gaussian symbols) are passed through a raised-root-cosine filter and modulated to different frequencies (433 MHz, 708 MHz, 2450 MHz). These measurements are carried out for varying signal-to-noise conditions (approximately 0 dB, 10 dB, 20 dB, estimated pre-measurement). These datasets may be used, for example, for data-driven channel modeling with state-of-the-art AI and machine learning algorithms (e.g., generative adversarial networks), for validating conventional channel models against real measurements, or for investigating the properties of real wireless channels. A detailed description is provided in the file README.pdf

Funding-Grants: Spanish Ministry of Economy and Competitiveness under Project RTI2018-099722-B-I00 (ARISTIDES)

Keywords

Deep Learning, Datasets, Channel Estimation, Wireless Channel Modelling

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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