Personal radio planning at the federal communications commission

1977 ◽  
Author(s):  
R.S. Stone
Keyword(s):  
Federal Communications Commission ◽  
Radio Planning

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Radio Spectrum Management and RFI in the United States

1991 ◽  
Vol 112 ◽  
pp. 174-175
Author(s):  
R. Marcus Price
Keyword(s):  
United States ◽  
Department Of Defense ◽  
Government Regulation ◽  
The United States ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Federal Communications Commission ◽  
Private Companies ◽  
The Government ◽  
Government Body

ABSTRACTIn the United States, civil common carrier telecommunications are provided by private companies, not by any agency of the government. Regulation of these services and spectrum management oversight is provided by the Federal Communications Commission (FCC), an agency of the government. Government telecommunications are operated by individual agencies, e.g. the Department of Defense, under the overall regulation of the Office of Spectrum Management of the National Telecommunications and Information Administration (NTIA), a government body separate from the FCC. In bands shared by the civil and government sectors, liaison and coordination is effected between the FCC and the NTIA.

scholarly journals Radio Planning and Optimization of W-CDMA Systems

2003 ◽  
pp. 437-447 ◽  
Author(s):  
Edoardo Amaldi ◽  
Antonio Capone ◽  
Federico Malucelli ◽  
Francesco Signori
Keyword(s):  
Cdma Systems ◽  
Radio Planning

Radio Planning of Single-Frequency Networks for Broadcasting Digital TV in Mixed-Terrain Regions

2014 ◽  
Vol 56 (6) ◽  
pp. 123-141 ◽  
Author(s):  
Nektarios Moraitis ◽  
Panagiotis N. Vasileiou ◽  
Constantine G. Kakoyiannis ◽  
Athanasios Marousis ◽  
Athanasios G. Kanatas ◽  
...  
Keyword(s):  
Digital Tv ◽  
Single Frequency ◽  
Radio Planning
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