scholarly journals Survey on the Research Challenges of Radio Resource Management in LTE-A and the Current Proposed Solutions for these Challenges

2013 ◽  
Vol 7 (3) ◽  
pp. 626-637
Author(s):  
Huthaifa Al-Jaradat ◽  
Kumbesan Sandrasegaran
Keyword(s):  
Resource Management ◽  
Packet Scheduling ◽  
Radio Resource Management ◽  
Interference Management ◽  
Radio Resource ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Lte Advanced ◽  
Technical Challenges

Long Term Evolution-Advanced (LTE-Advanced) has been recently submitted by the 3rd Generation Partnership Project (3GPP) to the International Telecommunication Union (ITU) as one of the candidates 4G technologies. LTE-Advanced is expected to outperform its predecessor (i.e. LTE) by providing data rate up to 1Gbps and 500 Mbps in the downlink and uplink directions, respectively, also by supporting higher speed mobility (i.e. 500 km/h). In order to allow such advances in the performance, Radio Resource Management (RRM) must be effectively utilized. This paper studies the technical challenges associated with some of the RRM tasks (including Packet scheduling, interference management and handover control), in addition it presents from the open literature some of the proposed solutions to these technical challenges.

Review of Radio Resource Management For IMT-Advanced System

2015 ◽  
Vol 72 (4) ◽  
Author(s):  
M. K. Ismail ◽  
A. A. M. Isa ◽  
M. S. Johal
Keyword(s):  
Resource Management ◽  
Packet Scheduling ◽  
Radio Resource Management ◽  
Mobile Wimax ◽  
Radio Resource ◽  
Air Interface ◽  
Lte Advanced ◽  
Radio Resources ◽  
Advanced System

This paper provides a review of radio resource management technologies that can be used or proposed for OFDMA-based IMT-Advanced system.  IMT-Advanced specifications significantly enhanced the existing IMT 2000 standard which are represented by LTE-Advanced and mobile WiMAX as the main successful candidates. One of the key components for OFDMA is Radio Resource Management (RRM) which is used to manage radio resources for the air interface in a cellular network. The main purpose of RRM is to utilize the available radio resources efficiently. The RRM tools such as power control, handover, packet scheduling, resource allocation and, load and admission control are needed to be controlled to maximize the performance efficiency in the wireless networks. By controlling these RRM tools, we are able to maximize the number of users in the network and let the users experience the best Quality of Service (QoS) and throughput.

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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