scholarly journals Adaptive radio resource management for maximizing reward and balancing loads in 4G hybrid universal mobile telecommunications system and long term evolution communications

2013 ◽  
Vol 15 (3) ◽  
pp. 510-526 ◽  
Author(s):  
Ben-Jye Chang ◽  
Ying-Hsin Liang ◽  
Kai-Xiang Cao
Keyword(s):  
Resource Management ◽  
Radio Resource Management ◽  
Long Term Evolution ◽  
Mobile Telecommunications ◽  
Radio Resource ◽  
Term Evolution ◽  
Universal Mobile Telecommunications System

An overview of downlink radio resource management for UTRAN long-term evolution

2009 ◽  
Vol 47 (7) ◽  
pp. 86-93 ◽  
Author(s):  
K.I. Pedersen ◽  
T.E. Kolding ◽  
F. Frederiksen ◽  
I.Z. Kovacs ◽  
D. Laselva ◽  
...  
Keyword(s):  
Resource Management ◽  
Radio Resource Management ◽  
Long Term Evolution ◽  
Radio Resource ◽  
Term Evolution

Radio Resource Management for Green 3GPP Long Term Evolution Cellular Networks: Review and Trade-offs

2013 ◽  
Vol 30 (3) ◽  
pp. 257 ◽  
Author(s):  
MustafaIsmael Salman ◽  
MuntadherQasim Abdulhasan ◽  
CheeKyun Ng ◽  
NorKamariah Noordin ◽  
Aduwati Sali ◽  
...  
Keyword(s):  
Resource Management ◽  
Cellular Networks ◽  
Radio Resource Management ◽  
Long Term Evolution ◽  
Radio Resource ◽  
Term Evolution ◽  
Trade Offs ◽  
3Gpp Long Term Evolution

scholarly journals QoS Oriented Multiobjective Optimizer for Radio Resource Management of LTE-A Femtocells

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Ayesha Haider Ali ◽  
Muhammad Mohsin Nazir
Keyword(s):  
Resource Management ◽  
Radio Resource Management ◽  
Optimal Solution ◽  
Limited Resources ◽  
Radio Resource ◽  
Term Evolution ◽  
Data Rates ◽  
Transmission Opportunity

The future wireless networks support multimedia applications and require ensuring quality of the services they provide. With increasing number of users, the radio resource is becoming scarce. Therefore, how should the demands for higher data rates with limited resources be met for Long Term Evolution-Advanced (LTE-A) is turning out to be a vital issue. In this research paper we have proposed an innovative approach for Radio Resource Management (RRM) that makes use of the evolutionary multiobjective optimization (MOO) technique for Quality of Service (QoS) facilitation and embeds it with the modern techniques for RRM. We have proposed a novel Multiobjective Optimizer (MOZ) that selects an optimal solution out of a Pareto optimal (PO) set in accordance with the users QoS requirements. We then elaborate the scheduling process and prove through performance evaluation that use of MOO can provide potential solutions for solving the problems for resource allocation in the advancement of LTE-A networks. Simulations are carried out using LTE-Sim simulator, and the results reveal that MOZ outperforms the reference algorithm in terms of throughput guarantees, delay bounds, and reduced packet loss. Additionally, it is capable of achieving higher throughput and lower delay by giving equal transmission opportunity to all users and achieves 100% accuracy in terms of selecting optimal solution.

scholarly journals RESOURCE ELEMENT-LEVEL DIMENSIONING OF LONG TERM EVOLUTION NETWORKS

2013 ◽  
Author(s):  
Arshed Oudah
Keyword(s):  
Long Term Evolution ◽  
Mobile Telecommunications ◽  
Element Level ◽  
Lte Networks ◽  
The Third ◽  
Term Evolution ◽  
Air Interface ◽  
Universal Mobile Telecommunications System ◽  
Cellular Technology

Long Term Evolution (LTE) of Universal Mobile Telecommunications System (UMTS) is the latest cellular technology coined by the third Generation Partnership Project (3GPP) and is currently being deployed worldwide. a crucial step towards the successful deployment scenarios of LTE networks is through the use of accurate budgetary and coverage dimensioning formulas of LTE’s physical air-interface channels and signals to guarantee favourable cell footprint and other capacity targets. In this paper, the total physical downlink resources along with their associated powers are derived and computed. Their corresponding overheads are also presented. The provided formulas serve in various network dimensioning campaigns of the LTE systems.  

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