image pixel

Year of Publication:

2015

Authors:

Jehad Hamamreh

Department of Telecommunication Engineering, Faculty of Engineering and Information Technology, An-Najah National University, Nablus. Palestine

Nader Menawi

Department of Telecommunication Engineering, Faculty of Engineering and Information Technology, An-Najah National University, Nablus. Palestine

Awni Natshi

Department of Telecommunication Engineering, Faculty of Engineering and Information Technology, An-Najah National University, Nablus. Palestine

Allam Mousa

Department of Telecommunication Engineering, Faculty of Engineering and Information Technology, An-Najah National University, Nablus. Palestine

Falah Hasan

Department of Telecommunication Engineering, Faculty of Engineering and Information Technology, An-Najah National University, Nablus. Palestine

Yousef Dama

Department of Telecommunication Engineering, Faculty of Engineering and Information Technology, An-Najah National University, Nablus. Palestine

[email protected]

Khaled Hijjeh

Telecom. Eng. Dep., An-Najah National University.

[email protected]

Preferred Abstract (Original):

One of the most important requirements for Long Term Evolution (LTE) is minimizing the costs and effort of network planning, optimization and configuration to the lowest possible level, while keeping a very good acceptable performance level which can be achieved by using self-organizing networks (SON) concept. This paper presents an efficient technique to train base station (E-NodeB) in order to choose the most appropriate and optimized scheduler in LTE system for each pixel inside an image using Neural Network technique, which leads to an optimized bandwidth and hence increased capacity. The simulation results using our proposed method using self-organizing assigning scheduler indicate an overall 33% saving in

An-Najah Staff

Self-Organizing Schedulers in LTE System for Optimized Pixel Throughput Using Neural Network