Energy efficient coordinated radio resource management: a two player sequential game modelling for the long-term evolution downlink

2012 ◽  
Vol 6 (14) ◽  
pp. 2239 ◽  
Author(s):  
C. Turyagyenda ◽  
T. O'Farrell ◽  
W. Guo
Keyword(s):  
Resource Management ◽  
Energy Efficient ◽  
Radio Resource Management ◽  
Long Term Evolution ◽  
Radio Resource ◽  
Sequential Game ◽  
Term Evolution

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.

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