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.
TABLE OF CONTENTS
APPROVAL PAGE.. ii
CERTIFICATION.. iii
DEDICATION.. iv
ACKNOWLEDGEMENTS. v
ABSTRACT. vi
TABLE OF CONTENTS. vii
LIST OF FIGURES. x
LIST OF TABLES. xii
LIST OF ACRONYMS. xiii
CHAPTER ONE.. 1
INTRODUCTION.. 1
1.0 STUDY BACKGROUND.. 1
1.1 OBJECTIVES OF THE RESEARCH.. 6
1.2 STATEMENT OF PROBLEM… 6
1.3 NEED FOR RESEARCH.. 6
1.4 SCOPE OF WORK.. 7
1.5 METHODOLOGY.. 7
1.6 THESIS OUTLINE.. 8
CHAPTER TWO.. 9
LITERATURE REVIEW… 9
2.1 CELLULAR NETWORK RESOURCE ALLOCATION.. 9
2.1.1 Allocation of Channel 9
2.1.2 Allocation of Bandwidth. 10
2.1.3 Allocation of Frequency. 11
2.2 RESOURCE ALLOCATION TECHNIQUES AND ALGORITHMS. 12
2.2.1 Resource Allocation Techniques using Neural Networks. 12
2.2.2 Resource Allocation Techniques using Genetic Algorithms. 13
2.2.3 Resource Allocation Techniques using Optimization Methods. 14
2.2.4 Resource Allocation Techniques using Evolutionary Algorithms. 15
2.2.5 Resource Allocation Techniques using Fuzzy Logic. 16
2.3 RESOURCE ALLOCATION PREDICTIVE MODELS. 16
2.4 ARTIFICIAL NEURAL NETWORK (ANN) 19
2.4.1 The Mathematical Model of ANN.. 19
2.4.2 Activation Functions. 20
2.4.3 ANN Architectures and Models. 20
2.5 SUMMARY.. 27
CHAPTER THREE.. 28
DEVELOPMENT OF ANN PREDICTIVE MODEL.. 28
3.1 INTRODUCTION.. 28
3.2 METHODOLOGY.. 28
3.2.1 Data Collection and Selection. 28
3.2.2 Data Pre-processing. 29
3.3 DATA TRAINING.. 31
3.4 DEVELOPMENT OF CELLULAR NETWORK RESOURCE ALLOCATION PREDICTIVE MODEL 32
3.4.1 Back propagation Feed Forward Neural Network Architecture. 32
3.4.2 Development of the Model 33
3.5 PROPOSED MODEL.. 40
3.6 METRICS FOR MEASURING PERFORMANCE.. 41
3.7 MODEL VALIDATION.. 42
3.8 ERLANG-B FORMULA.. 42
CHAPTER FOUR.. 44
SIMULATION RESULTS AND RESULTS ANALYSIS. 44
4.1 INTRODUCTION.. 44
4.2 PRE-PROCESSING RESULTS AND ANALYSIS. 44
4.2.1 Transformation and Visualization. 44
4.2.2 Clustering. 49
4.3 CONFIGURATION OF THE DEVELOPED MODEL.. 51
4.4 TRAINING PARAMETERS. 61
4.5 VALIDATION OF THE ANN MODEL.. 62
4.6 NETWORK RESOURCE PREDICTION USING THE DEVELOPED ANN MODEL 62
CHAPTER FIVE.. 64
APPLICATION TESTING AND PERFORMANCE ANALYSIS. 64
5.0 INTRODUCTION.. 64
5.1 TESTING THE APPLICATION WITH DIFFERENT INPUT-TARGET SCENARIOS 64
5.1.1 Linear regression. 65
5.1.2 Correlation. 80
5.2 PREDICTED TRAFFIC AND CHANNEL ALLOCATION.. 85
5.3 APPLICATION PERFORMANCE AND SECTOR CLUSTERING.. 102
5.4 COST OF DEVELOPING THE APPLICATION.. 104
CHAPTER SIX.. 105
MODEL DEPLOYMENT. 105
6.0 INTRODUCTION.. 105
6.1 DEVELOPMENT OF THE ANN-BASED NETWORK RESOURCE ALLOCATION APPLICATION 105
6.1.1 Layout of the Graphical User Interface. 105
6.1.2 Programming of the GUI. 108
6.2 PACKAGING THE ANN-BASED NETWORK RESOURCE ALLOCATION APP FOR DEPLOYMENT. …. 113
6.3 INSTALLING THE NETWORK RESOURCE ALLOCATION APPLICATION.. 116
CHAPTER SEVEN.. 120
CONCLUSION.. 120
7.0 INTRODUCTION.. 120
7.1 CONCLUSIONS. 120
7.2 CONTRIBUTIONS TO KNOWLEDGE.. 121
7.2.1 List of Publications. 121
7.3 DIRECTIONS FOR FUTURE RESEARCH.. 122
REFERENCES. 123
APPENDIX I. 133
MATLAB Code for Network Resource Allocation Module. 133
APPENDIX II. 140
Location Data. 140
APPENDIX III. 142
MATLAB Code for Inverse Erlang-B.. 142
APPENDIX IV.. 143
Codebook Vector 143
APPENDIX V.. 146
GSM Logical Channels 146
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.
The network subsystem provides overall control and interfacing of the whole mobile network. It comprises mainly of the Mobile Switching Centre (MSC) which acts like a normal switching node in a telephone exchange [119]. It also, performs other tasks for a mobile phone like registration, authentication, call location and routing using the Visitor Location Register (VLR), Home Location Register (HLR), Equipment Identity Register (EIR) and Authentication Centre (AuC).
Other important elements contained in the network subsystem are the Gateway Mobile Switching Centre (GMSC) and Message Service Gateway (SMS-G). The GMSC terminates call initially routed to the network without knowledge of the location of the mobile phone while the SMS-G handles and directs messages in different directions [121].
Established cellular technologies exist in the telecom industry and they include the Global System for Mobile Communication (GSM), and code division multiple access (CDMA). GSM is the most widely used wireless cellular technology [122]. Its family of technologies include General Packet Radio Service (GPRS), Enhanced Data for GSM Evolution (EDGE), Universal Mobile Telecommunication System (UMTS), High Speed Packet Access (HSPA), and more recently Long Term Evolution (LTE) network [122].
The major players in the telecom industry are the regulators, operators, and subscribers [137]. Regulators provide the framework and general rules of operation in the industry. Such regulators include the International Telecommunication Union (ITU), and several other regulatory bodies in different countries of the world. The Nigerian Communication Commission (NCC) is the main regulatory body saddled with the responsibility of supervising the operation of mobile cellular network operators in Nigeria.
Cellular network operators provide services to users that subscribe to their network. They are majorly classified into mobile and fixed wireless operations. Several operators exist in different countries, a list of such operators are available [1, 2]. In Nigeria, the major operators are in the area of GSM and they comprise of Airtel, MTN, GLO, and Etisalat. The fifth mobile operator, Mtel, is now moribund [3].
The subscribers are the end users of the services provided by the mobile operators. All over the world the number of subscribers has been increasing steadily. Taking Nigeria as a typical example, mobile cellular network was introduced in 2001 and since then figures from the NCC show tremendous growth of connected lines (subscribers). As at March 2014 connected lines stand at 124,884,842 for GSM, 2,039,391 for CDMA, and 172,963 for Fixed/Fixed Wireless from only 266,461 connected subscribers at inception [3]. The tele density grew from a mere 0.73 to 92.24 within the period. With a population of about 170 million people and an annual growth projection of 2.54 %, the number of subscribers is bound to rise [4, 5]. Similar results were obtained in other developing countries [123].
Over the years, several advances have been made in cellular communication technology, with increasingly capable user terminals, and expanding range of mobile applications. This growth has led to significant increase in voice and data traffic with different Quality of Service (QoS) requirements. Understanding the characteristics of the traffic is important for network design, traffic modelling, resource planning and resource control. The ITU and other regulatory agencies have laid out the requirements and framework for effective service delivery in mobile cellular networks [126].
Provision of services to subscribers by cellular network providers require adequate resources such as bandwidth, radio links, traffic channels, which are usually scarce and expensive. For this reason, the regulators and operators of mobile wireless cellular networks have continually been looking for ways to improve on network QoS standard. This they do by constantly searching for the most effective, efficient and proactive cellular network resource allocation scheme that will deliver the best QoS values to network subscribers [128, 129, 132].
The ITU Recommendation E.507 provides an overview of the existing mathematical techniques for modelling and forecasting. Such techniques include curve fitting, Autoregressive Integrated Moving Average (ARIMA), state space models with Kalman filtering, regression and econometric models [6]. It also describes methods for the evaluation of the forecasting models and for the choice of the most appropriate method in each case, depending on the available data, length of the forecast period, etc [6].
There are many resource allocation techniques existing in literature. Majorly, these are based on fixed or dynamic allocation schemes. In fixed allocation systems the resources are predetermined and are assigned manually by operators [7, 8]. In dynamic allocation schemes the allocation of resources is varied according to needs and demands and this is usually achieved by means of an algorithm [9, 10, 11].
In recent times, Artificial Neural Network (ANN) algorithms have been used without complexities in other fields for the analysis of similar problems in related areas using data sets [12 – 27]. It is on this basis that the ANN technique which greatly reduces the complexity in data processing and analysis without any infringement on system’s precision is intended to be applied in the realization of a predictive model that can be used for cellular network resource allocation.
A predictive model is a statistical model created to forecast future behaviour based on present and/or existing indicators [124]. Predictive models are made up of a number of predictors, which are variable factors that are likely to influence future behaviour or results. Predictive modelling, therefore, is an area of data mining concerned with forecasting probabilities and trends [125]. In predictive modelling, data is collected for the relevant predictors, a model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. The model may employ a simple linear equation, statistics, machine learning, neural computing, robotics, computational mathematics, and artificial intelligence techniques or a complex neural network, mapped out by sophisticated software.
Predictive modelling explores all the data set instead of a narrow subset to bring out meaningful relationships and patterns, it can be applied in various areas such as customer relationship management, capacity planning, change management, disaster recovery, security management, engineering, meteorology and city planning [28].
There are two major types of predictive modelling approaches – those with supervised learning and the others with unsupervised learning. In supervised learning, predictive models are created using a set of trained data that contains results upon which the prediction will be based [117]. The techniques used for this type of learning include classification, regression, and time-series analysis. The classification identifies groups within the data and associates any new data with a group, regression uses past values to predict future values, while time-series uses time to predict seasonal variances.
Unsupervised learning on the other hand does not use previous results to train its models; rather it uses descriptive statistics to examine the natural patterns existing within the data [124, 125]. The techniques used here include clustering, and association. Clustering identifies groups of similar behaviour within the data, while association identifies the relationship among various groups within the data.
In general, predictive models can be built by supervised learning and/or unsupervised learning using several techniques [125]. The models can be implemented using a variety of algorithms suited for different data or problems. Many software packages are available that implement this models and algorithms to find the best combination that works. These software packages can be classified into proprietary and open source. Proprietary software are those licensed by companies and are usually very expensive; open source software on the other hand can be freely downloaded from the internet under the GNU agreement. Examples of proprietary software include MATLAB, Statistica, MapleSim, Mathematica, IBM SPSS Modeler, SAS Enterprise Miner, and Microsoft SQL Server [29 – 35]. Open Source software includes Knime, Orange, Weka, R, and RapidMiner [36 – 40].
The development of a cellular network predictive system will be based on a number of predictors, which are variable factors that influence subscribers’ resource demand behaviour in the future using historical data. The use of this data is imperative because the intrinsic behaviour of a network is usually embedded in the data collected from the network over time [41].
The multidimensional data collected will be analysed simultaneously to observe emerging network trends and performance. Such predictions and also the requirement to analyse multidimensional data simultaneously has an overwhelming influence on the traditional data analysis methods. This results in complex data processing and data analysis that are usually difficult to track and consequently leads to erroneous resource allocation decisions. Hence the choice of ANN becomes advantageous as it shows excellent performances in similar circumstances.
The task, therefore, will be the development of a predictive model that is based on very influential predictors which are well known Key Performance Indicators (KPIs) like call setup success rate (cssr), drop call rate (dcr), standalone dedicated channel (SDCCH) blocking rate (sdcchblk), SDCCH loss rate (sdcchloss), handover success rate (hosr), call setup blocking rate (callsetblk), traffic channel blocking rate (tchblk), traffic channelmean traffic (tchmean).
This were selected based on a meticulous analysis of the GSM logical channels from where the KPIs were abstracted. The multidimensional data collected will be analysed simultaneously to observe emerging network trends and performance. Such predictions and also the requirement to analyse multidimensional data simultaneously has an overwhelming influence on the traditional data analysis methods. This results in complex data processing and data analysis that are usually difficult to track and consequently leads to erroneous resource allocation decisions. Hence the choice of ANN becomes advantageous as it shows excellent performances in similar circumstances.
1.1 OBJECTIVES OF THE RESEARCH
The objective of this research is to develop a cellular network predictive system that,
- provides the required QoS parameters values to the network subscribers at relatively affordable price.
- maximizes the utilization of cellular network resources and thereby maximizing revenue for the network providers.
- is capable of being integrated into a typical mobile wireless cellular network with ease.
- responds to random network resource demands instantly and with precision.
- delivers to the network providers a resource management system that is relatively simple, efficient and effective.
1.2 STATEMENT OF PROBLEM
The growing number of subscribers and increased access to mobile user terminals and/or devices has caused a strain on the usage of network resources required to provide satisfactory QoS needs such as bandwidth, traffic channels, and radio links. Most resource allocation schemes do not address the issue of resource utilization and availability in terms of physical resources, these schemes tend to focus on time rather than the resources even though time too is critical. A balance is therefore required between making resource available in the right quantity when needed. This saves cost as resource will be provided based on actual need. These resources are scarce, limited and expensive; hence it becomes imperative that network resources be properly and intelligently allocate for optimal performance.
1.3 NEED FOR RESEARCH
The regulators and operators of mobile wireless cellular networks have both come to settle with continually looking for ways to improve on network Quality of Service (QoS) standard by continuous search for network resource allocation schemes that will deliver the best QoS values to network subscribers. A proactive resource allocation scheme is required to predict the future behaviour of the network and properly allocate resources efficiently in the face of competing demands. These allocations should be dynamic enough to simultaneously look at other QoS requirements in order to react appropriately to the usual random network resource demands. Therefore, the need to develop a cellular network resource allocation predictive system capable of predicting the future network traffic volume/intensity and accurately determining the optimum quantity of resources to be allocated becomes necessary. Proper allocation of these resources will minimize call drops [110].
1.4 SCOPE OF WORK
The scope of this thesis will be limited to the application of neural network to the development of resource predictive model that predicts future mean traffic intensity in a cellular network from its historical data. From the predictions made, channels will be allocated to cells within the network in advance to efficiently service the predicted traffic.
Finally, the model will be implemented by developing an Application software that can be deployed in a cellular network environment to predict the future mean traffic and to allocate channels that will be required to service the predicted traffic intensity based on the inverse Erlang-B formula.
1.5 METHODOLOGY
The methodology adopted a research and development design. The ANN based cellular network resource allocation predictive system model will be developed using data collected, for a period of twelve (12) months, from an established typical cellular network operator in Nsukka, Nigeria,. The historical data will be pre-processed, trained and analysed using the Self-Organizing Map (SOM) Toolbox [114] and the Neural Network Toolbox [116] in a MATLAB environment. The model is formulated as an n-layer Feed-Forward ANN with seven predictors as inputs, a hidden layer and an output variable. The predictors are: cssr, dcr, sdcchblk, sdcchloss, hosr, callsetblk, and tchblk. The application of up to seven predictors as input is to further improve on the accuracy of the system’s prediction capability which, in turn, will lead to accurate resource allocation decisions. The target outcome will be based on tchmean.
Rigorous analysis will be carried out to select a suitable and best performing algorithm with adequate number of neurons which will be adopted as the model. The inverse Erlang-B formula will be used in the model to determine the amount of resource required to meet QoS requirements. The performance of the developed ANN-based Model in predicting the future mean traffic in each sector will be validated by comparing it with some existing techniques using the 10 fold cross-validation method. This validation is important to assess how the results will generalize to an independent data set. The Mean Squared Error (MSE), Mean Absolute Error (MAE) and regression analysis will be used as indicators to measure the performance.
The model will then be tested through simulation in a MATLAB environment and the test results ploughed back into the model for modification and further finer performance improvement. Finally, the model will be implemented as an Application software for integration and deployment in the cellular network using the Graphical User Interface Development Environment (GUIDE) in MATLAB.
1.6 THESIS OUTLINE
The rest of the thesis is organized as follows: Chapter 2 presents the literature review of cellular network resource allocation, techniques, algorithms and models. Related research works based on network resource predictive models are also presented. In Chapter 3, the methodology and processes leading to the development of the ANN based predictive model are presented. Starting with the description of the area of study, through how the data was collected, pre-processed, trained and ended with the development and validation of ANN-based model based on detailed analysis. Chapter 4 presents simulation results and results analysis of the results obtained from some processes in Chapter 3. These includes results from pre-processing, training, and the developed model. The testing of the developed model and statistical analysis of its performances in different input-target scenarios are presented in Chapter 5. Chapter 6, presents the steps leading to the development, packaging and deployment of the Application software that can be installed on a network system for the purpose of predicting future mean traffic of an existing network and the number of traffic channels required to adequately service the needs based on given QoS requirements. In Chapter 7, conclusions, recommendations and direction for future work are presented.
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