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.

scholarly journals COMPARATIVE ANALYSIS OF THE PERFORMANCE OF RESOURCE ALLOCATION ALGORITHMS IN LONG TERM EVOLUTION NETWORKS

2016 ◽  
Vol 36 (1) ◽  
pp. 163-171
Author(s):  
UN Nwawelu ◽  
CI Ani ◽  
MA Ahaneku
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Long Term Evolution ◽  
Proportional Fair ◽  
Term Evolution ◽  
Allocation Algorithms ◽  
Resource Allocation Schemes ◽  
Radio Resource Scheduling ◽  
Good Number

The growth in the good number of real-time and non-real-time applications has sparked a renewed interest in exploring resource allocation schemes that can be efficient and fair to all the applications in overloaded scenarios. In this paper, the performance of six scheduling algorithms for Long Term Evolution (LTE) downlink networks were analyzed and compared. These algorithms are Proportional Fair (PF), Exponential/Proportional Fair (EXP/PF), Maximum Largest Weighted Delay First (MLWDF), Frame Level Scheduler (FLS), Exponential (EXP) rule and Logarithmic (LOG) rule.  The performances of these algorithms were evaluated using an open source simulator (LTE simulator) and compared based on network parameters which include: throughput, delay, Packet Loss Ratio (PLR), and fairness. This work aims at giving insight on the gains made on radio resource scheduling for LTE network and to x-ray the issues that require improvement in order to provide better performance to the users. The results of this work show that FLS algorithm outperforms other algorithms in terms of delay, PLR, throughput, and fairness for VoIP and video flow. It was also observed that for Best Effort (BE) flows, FLS outperforms other algorithms in terms of delay and PLR but performed least in terms of throughput and fairness. http://dx.doi.org/10.4314/njt.v36i1.21

Realization of Primitive Trace in TD-LTE RF Conformance Testing Instrument

2014 ◽  
Vol 513-517 ◽  
pp. 893-896
Author(s):  
Li Qiang Guo ◽  
Xiao Wen Li
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Long Term Evolution ◽  
Conformance Testing ◽  
Testing Instrument ◽  
Hardware Platform ◽  
Term Evolution

In order to satisfy the requirement of reliability in the TD-LTE RF Conformance Testing Instrument, a method to achieve the trace of primitive of TD-LTE based on embedded system is imposed. According to the feature of the long term evolution (LTE) system, it highlights the details of primitive tracing through DMA and UART. The primitive is strongly real-time to achieve QoS in LTE. Verified multiple times this design achieves the primitive trace in LTE as well as meets the requirement of hardware platform in TD-LTE RF Conformance Testing Instrument.

Long-term evolution and early warning strategies for complex rockslides by real-time monitoring

Landslides ◽  
2017 ◽  
Vol 14 (5) ◽  
pp. 1615-1632 ◽  
Author(s):  
G. B. Crosta ◽  
F. Agliardi ◽  
C. Rivolta ◽  
S. Alberti ◽  
L. Dei Cas
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Long Term Evolution ◽  
Real Time Monitoring ◽  
Term Evolution

Simulation of Packet Scheduling in Cognitive Long Term Evolution-Advanced

2017 ◽  
Vol 8 (2) ◽  
pp. 533
Author(s):  
Mohamad ‘Ismat Hafizi Mansor ◽  
Huda Adibah Mohd Ramli ◽  
Ani Liza Asnawi ◽  
Farah Nadia Mohd Isa
Keyword(s):  
Real Time ◽  
Packet Scheduling ◽  
Long Term Evolution ◽  
Multimedia Content ◽  
Multimedia Contents ◽  
Term Evolution ◽  
Spectrum Scarcity ◽  
Qos Requirement ◽  
Long Term Evolution Advanced

<p>Real Time (RT) and Non-Real Time (NRT) multimedia content demand on mobile devices are increasing at a high pace. Long Term Evolution-Advanced (LTE-A) is expected to cater these demands. However, LTE-A operates at fixed spectrum which leads to spectrum scarcity. Cognitive Radio (CR) is one the promising technologies that is used to overcome spectrum scarcity and implementation of CR into LTE-A will improve spectrum availability and efficiency of the network. Furthermore, with addition of Packet Scheduling (PS) in the cognitive LTE-A, QoS requirement of the mobile users can be guaranteed. However, the study on the stated is very limited. Thus, this paper models, simulates and evaluates performance of five well-known PS algorithms for supporting the RT and NRT multimedia contents. The simulation results show that Maximum- Largest Weighted Delay First (M-LWDF) is the best candidate for implementation in the cognitive LTE-A.</p>

Quality of service analysis of video streaming in fourth generation-Long Term Evolution for telemedicine applications: An experimental learning with game theory approach

2021 ◽  
pp. 002072092098849
Author(s):  
Panjavarnam B ◽  
Bhuvaneswari PTV
Keyword(s):  
Game Theory ◽  
Quality Of Service ◽  
Real Time ◽  
Long Term Evolution ◽  
Fourth Generation ◽  
Theory Approach ◽  
Teaching Pedagogy ◽  
Term Evolution

Wireless technologies have become the model for Entertainment, Communication and Education all over the world. Electrical and Electronics Engineering Departments of various Universities across the globe offer courses on Wireless Communication and Networks for Undergraduate and Postgraduate students by focusing on the latest teaching pedagogy. Recent advancements in health care services demand reliable transmission of medical images and videos for better diagnosis and treatment. The effective utilization of available spectrum is one of the requirements for stable transmission of medical information. A Long Term Evolution (LTE) system model for a real-time multimedia application must provide high Quality of Service (QoS). Scheduling and allocating resources for real-time downlink flows in LTE networks is a challenging issue, as the network excellence and assured output have to be confirmed together with decreasing the Head of Line (HOL) suspension. The ever increasing demand for higher data rates in uplink and downlink transmission services constraints on delay and bandwidth requirements. This leads the existing fourth generation LTE wireless networks to concentrate more on the resource scheduling techniques with acceptable levels of Quality of Service (QoS). In this paper, a Game theory based scheduling (GTBS) and resource allocation Algorithm multimedia traffic in LTE network is designed and studied. The Algorithm is simulated in LTE-sim for real time (RT) and Non-real time (NRT) data traffic. The work identified a new teaching pedagogy, the work is explained to the undergraduate Engineering students so that they can apply the knowledge to develop new innovative projects.

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