Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning - Publikacja - MOST Wiedzy

Twoje konto

zaloguj

Wyszukiwarka

Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Abstrakt

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.

Cytowania

  • 0

    CrossRef

  • 0

    Web of Science

  • 0

    Scopus

Autorzy (5)

Cytuj jako

Pełna treść

pobierz publikację
pobrano 78 razy
Wersja publikacji
Accepted albo Published Version
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s21227716
Licencja
Creative Commons: CC-BY otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
SENSORS nr 21,
ISSN: 1424-8220
Język:
angielski
Rok wydania:
2021
Opis bibliograficzny:
Cwalina K., Rajchowski P., Olejniczak A., Błaszkiewicz O., Burczyk R.: Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning// SENSORS -,iss. 22 (2021),
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s21227716
Źródła finansowania:
Weryfikacja:
Politechnika Gdańska

wyświetlono 138 razy

Publikacje, które mogą cię zainteresować

Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach

2022

User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning

2024

An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key

2019

Self-Supervised Learning to Increase the Performance of Skin Lesion Classification

2020
Meta Tagi