ARTIFICIAL NEURAL NETWORK-BASED CELLULAR NETWORK PREDICTIVE SYSTEM FOR RESOURCE ALLOCATION

ARTIFICIAL NEURAL NETWORK-BASED CELLULAR NETWORK PREDICTIVE SYSTEM FOR RESOURCE ALLOCATION

ABSTRACT

A cellular network resource allocation predictive system based on artificial neural network (ANN) is presented. The predictive system is capable of predicting the future network traffic volume/intensity in a cell and accurately determining the optimum quantity of resources to be allocated to the cell to meet QoS demands. The main objective of this research is to develop a predictive system that delivers to the network providers a resource management system that is relatively simple, efficient and effective. The ANN based resource allocation predictive model was developed using data collected from an established cellular network operator in Nigeria. The data was pre-processed, trained and analysed using the Self-Organizing Map (SOM) and the Neural Network Toolboxes in a MATLAB environment. The model was formulated as a 3-layer Feed-Forward ANN network with seven predictors as inputs, a hidden layer and an output variable. After rigourous analysis, the Conjugate Gradient with Polak-Ribiere Restarts (CGP) configuration with 14 neurons in the hidden layer was finally adopted as the model. The performance of the model in predicting the future mean traffic in each cell was further compared with some existing techniques using the cross-validation method. The mean square error (MSE) and mean average error (MAE) values for the techniques were respectively found to be: single tree (43.18, 3.70), tree boost (45.26, 3.51), multilayer perceptron (44.83, 3.81), general regression neural network (35.35, 3.50), radial basis function (63.01, 4.92), general method of data handling polynomial network (17616, 54.11), support vector machine (40.43, 3.20), gene expression programming (26.41, 3.13), ANN Model (1.60, 1.31). The values obtained showed that the prediction capability of the developed model was superior to the existing techniques. The model was then tested through simulation in a MATLAB environment and the test results ploughed back into the model for modification and further finer performance improvement. Using the predicted mean traffic and applying the blocking probability as a QoS parameter, the ANN Model computes the traffic channel(s) to be allocated to each cell. Finally, the model was packaged as an Application software for integration into the cellular network using the Graphical User Interface Development Environment (GUIDE). The developed Application can fit easily into a cellular network system and it was successfully used to predict the number of channels needed to service a given cell based on the required QoS parameter values.

CHAPTER ONE

INTRODUCTION

1.0    STUDY BACKGROUND

A cellular network is a radio network comprising of cells which are interconnected usually over a large area spanning several kilometres [118]. These cells contain base transceiver stations (BTS) which enables the transmission and reception of radio signals to and from mobile user equipment usually referred to as mobile station (MS) such as mobile phones. These cells together provide radio coverage over a given geographical area.

The architecture for mobile cellular network is mainly divided into three subsystems: the MS, BTS, and network [119]. It can be further structured into a number of sections: Network and Switching Subsystem (NSS), Operations Support System (OSS), servers, Operation and Maintenance (O & M). Each subsystem performs its separate functions which are linked together by logical and physical channels to enable full operational capability of the system.

The MS otherwise called mobile phone or ‘handset’ is the part of mobile cellular network that the subscriber uses to communicate. It consists mainly of the hardware and subscriber identity module (SIM) [119]. The hardware comprises of all the electronics needed to generate, transmit, receive and process signals between the MS and BTS. The SIM provides the information that identifies the user to the network using the international mobile subscriber identity (IMSI) system.

The base station subsystem (BSS) consists of the base transceiver station (BTS) and base station controller (BSC) [120]. The BTS uses antennas, which are made up of transmitters and receivers, for direct communication with the MS through a special interface. The BSC manages the radio resources and controls a group of BTSs and also manages handovers and the allocation of channels in a network.

ARTIFICIAL NEURAL NETWORK-BASED CELLULAR NETWORK PREDICTIVE SYSTEM FOR RESOURCE ALLOCATION