UMTS Radio Interface Description

2005 ◽  
pp. 47-118
Keyword(s):  
Radio Interface

scholarly journals 5G New Radio Key Performance Indicators Evaluation for IMT-2020 Radio Interface Technology

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
A. Phani Kumar Reddy ◽  
M Sheeba Kumari ◽  
Vishakha Dhanwani ◽  
Akhil Kalpesh Bachkaniwala ◽  
Navin Kumar ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Key Performance Indicators ◽  
Radio Interface ◽  
New Radio

Radio-Interface Architecture

2018 ◽  
pp. 73-102
Author(s):  
Erik Dahlman ◽  
Stefan Parkvall ◽  
Johan Sköld
Keyword(s):  
Radio Interface

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

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7716
Author(s):  
Krzysztof K. Cwalina ◽  
Piotr Rajchowski ◽  
Alicja Olejniczak ◽  
Olga Błaszkiewicz ◽  
Robert Burczyk
Keyword(s):  
Deep Learning ◽  
Interface Design ◽  
Mean Squared Error ◽  
Radio Channel ◽  
Computational Cost ◽  
Urban Environments ◽  
Data Allocation ◽  
Urban Networks ◽  
Radio Interface ◽  
Channel State Estimation

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.

Radio-Interface Architecture

2014 ◽  
pp. 121-139
Author(s):  
Erik Dahlman ◽  
Stefan Parkvall ◽  
Johan Sköld
Keyword(s):  
Radio Interface
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