Performance Analysis of Long Term Evolution (LTE) Medium Access Control (MAC) Scheduler for Real-Time Heterogeneous Data Traffic

2019 ◽  
Author(s):  
Anjali D. Channagire ◽  
Anand D. Mane
Keyword(s):  
Performance Analysis ◽  
Access Control ◽  
Medium Access Control ◽  
Real Time ◽  
Long Term Evolution ◽  
Heterogeneous Data ◽  
Data Traffic ◽  
Medium Access ◽  
Term Evolution

Applying Genetic Algorithms to the Data Traffic Scheduling and Performance Analysis of a Long-Term Evolution System

2014 ◽  
Vol 81 (1) ◽  
pp. 387-403 ◽  
Author(s):  
Hsien-Wei Tseng ◽  
Yang-Han Lee ◽  
Chih-Yuan Lo ◽  
Liang-Yu Yen ◽  
Yih-Guang Jan
Keyword(s):  
Genetic Algorithms ◽  
Performance Analysis ◽  
Long Term Evolution ◽  
Evolution System ◽  
Data Traffic ◽  
Traffic Scheduling ◽  
Term Evolution ◽  
And Performance

Performance Analysis of MIMO Schemes in 3GPP Long Term Evolution System

2015 ◽  
Vol 82 (2) ◽  
pp. 1107-1125 ◽  
Author(s):  
Ali Jemmali ◽  
Mohammad Torabi ◽  
Jean Conan
Keyword(s):  
Performance Analysis ◽  
Long Term Evolution ◽  
Evolution System ◽  
Term Evolution ◽  
3Gpp Long Term Evolution

scholarly journals Performance Analysis of Fair Medium Access Control Protocol for Asymmetric Full Duplex in WLAN

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 140546-140557 ◽  
Author(s):  
Jin-Ki Kim ◽  
Won-Jae Lee ◽  
Chan-Byoung Chae ◽  
Jae-Hyun Kim
Keyword(s):  
Performance Analysis ◽  
Access Control ◽  
Medium Access Control ◽  
Medium Access Control Protocol ◽  
Full Duplex ◽  
Medium Access ◽  
Control Protocol

scholarly journals Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5327 ◽  
Author(s):  
Byoungsuk Ji ◽  
Ellen J. Hong
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Road Traffic ◽  
Learning Model ◽  
Long Term Evolution ◽  
The Road ◽  
Term Evolution ◽  
Traffic Speed ◽  
Access Data

In this paper, we propose a method for deep-learning-based real-time road traffic predictions using long-term evolution (LTE) access data. The proposed system generates a road traffic speed learning model based on road speed data and historical LTE data collected from a plurality of base stations located within a predetermined radius from the road. Real-time LTE data were the input for the generated learning model in order to predict the real-time speed of traffic. Since the system was developed using a time-series-based road traffic speed learning model based on LTE data from the past, it is possible for it to be used for a road where the environment has changed. Moreover, even on roads where the collection of traffic data is invalid, such as a radio shadow area, it is possible to directly enter real-time wireless communications data into the traffic speed learning model to predict the traffic speed on the road in real time, and in turn, raise the accuracy of real-time road traffic predictions.

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