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he challenge of optimal resource allocation to subscribers ofmobile Worldwide Interoperability for Microwave Access (WiMAX) has not been fully overcome by researchers. This research work developed an optimal scheduling algorithm for WiMAX resource allocation based on an improved Particle Swarm Optimization (PSO) technique. In this work, an improved PSO based technique for allocating subcarriers and Orthogonal Frequency Division Multiplexing (OFDM) symbols to mobileWiMAX subscriberswasdeveloped using sub-group formation. The entire WiMAX network environment was sub-divided into 7 layers.Seven distinct modulation and coding schemes were used in transmitting packets to the subscribers located within the respective layers. The objective function was determined based on PSO for throughput maximization and channel data rate. An enhanced model for throughput maximization and channel data ratewas developed by implementing an improved PSO based WiMAX resource allocation algorithm. Simulation of different scenarios of WiMAX multicast service to mobile subscribers for the evaluation of Aggregate Data Rate (ADR) and Channel Data Rate (CDR) for each scenario were carried out.The results obtained for the various layers and uniform distribution of users over the entire layers based on the performance evaluation of the improved algorithm for ADR were 350Mbps, 525Mbps, 700Mbps, 1050Mbps, 1050Mbps, 1400Mbps, 1575Mbps and 1398Mbps. Similarly, for CDR the results obtained were 6.98Mbps, 10.48Mbps, 13.97Mbps, 20.95Mbps, 20.95Mbps, 27.94Mbps, 31.5Mbps and 28Mbps. Validation was done by comparing the results obtained using the improved algorithmwith those of Maximum Throughput Algorithm (MTA). The values of ADR obtained based on the published work of Araniti et al., (2012) using the developed algorithm when users were randomly distributed and restricted to exist within each of the layers 1, 4 and 7 were 694Mbps, 175Mbps, 525Mbps and 788Mbps. Similarly, the results obtained for the CDRwere 13.9Mbps, 3.5Mbps, 10.5Mbps and 15.8Mbps. The corresponding values for the MTA were 400Mbps, 100Mbps, 400Mbps, 500Mbps and 12.5Mbps, 2.5Mbps, 12.5Mbps, 12.5Mbps, respectively. ADR of 694Mbps was achieved, which represented 88.88% of the maximum achievable ADR of the system as compared to 400Mbps, which represented 80% of the maximum achievable ADR obtained using MTA. This showed that the developed algorithm performed better than the MTA by 8.88%.




1.1 Background 1
1.2 Statement of problem 6
1.3 Aim and Objectives 6
1.4 Methodology 7
1.5 Significant Contribution 7
1.6 Scope of the Dissertation 8
1.7 Dissertation Organization 8
2.1 Introduction 9
2.2 Review of Fundamental Concepts 9
2.2.1 Multicast 9
2.2.2 IP Multicast 10
2.2.3 WiMAX Internet Protocol Television 11
2.2.4 Types of IPTV 11
2.1.5 WiMAX IPTV principle of operation 12
2.2.5 WiMAX Networks 14
2.2.6 Subgroup- based Resource Distribution in WiMAX Networks 19 Channel Monitoring 20 Subgroup Creation 22 Radio Identification Resource Distribution 23 Radio-Detection Subgrouping Techniques 23
viii Maximum Throughput 24 Proportional Fairness 26 Conventional Multicast Scheme (CMS) 27
2.2.7 Channel Modeling 27 COST-231 Hata Model 27
2.2.8 Radio Channel Characteristics 29
2.2.9 Radio Channel Diversity 30
2.2.10 Time-Varying Channel 32
2.2.11 Multiuser Diversity 32
2.2.12 Adaptive Modulation and Dynamic Resource Allocation 32
2.2.13 Review of Scheduling Algorithms 34
2.2.14 Review of Optimization Techniques 35 Genetic Algorithm (GA) 36 Ant Colony Optimization (ACO) 37 Tabu Search (TS) 38 Particle Swarm Optimization (PSO) 39
2.3 Review of Similar Works 41
3.1 Introduction 48
3.2. Proposed WiMAX Network Environment 48
3.2.1 Layer Data Rate 50
3.2.2 WiMAX Subscriber Substations 52 User Distribution Parameters 53
3.2.3 PSO base Objective Function 54 Throughput (Fobj) 55
3.2.4 Proposed PSO Algorithm for Throughput Maximization 58
3.2.5 Development of Improved PSO 60
4.1 Introduction 66
4.2 Aggregate Data Rate 66
4.3 Summary of Results Obtained from Improved PSO Algorithm 72
4.4 Performance Evaluation of Improved Algorithm and Maximum Throughput Algorithm 73
4.5 Summary of Results Obtained based on the Parameters Published in Work of Araniti et al., (2012) using the Improved Algorithm 75
4.6 Comparison of Results 76
4.7 Validation 80
5.1 Introduction 82
5.2 Summary of Findings 82
5.3 Conclusion 82
5.4 Limitations 83
5.5 Recommendations for Further Work 83
m. file ‗circles‘ 90
m. file ‗mtobjectivefunc‘ 91
m. file ‗s_sub station‘ 93




1.1 Background
Rising demand for high speed multimedia services like Internet Protocol Television(IPTV) and mobile television has led to the introduction of broadband wireless access (Giacomini & Agarwal, 2013).One of such broadband wireless access is the Worldwide Interoperability for Microwave Access(WiMAX), which is also known as IEEE 802.16. The standard enables high-speed access to data, video and voice services (Chaariet al, 2012). Internet Protocol Television (IPTV) is expected to bring aboutgreat market value to the service providers in 4 generation (4G) wireless network (Houet al., 2009). It also serves as another possibility to cabled access networks, such as fiber optics, coaxial systems using cable modems and Digital Subscriber Lines (DSL) (Adebari& Bello, 2013). WiMAX has a wide coverage range per BS which can covers up to 30 miles in radius (Shu‘aibu et al., 2011). WiMAX requires changing and diverse Quality of Service (QoS) guarantee such as optimal system throughput, maximum latencyguarantees and minimal delay jitter (Genc et al., 2008). Some of the QoS that need to be defined for the purpose of this research work are:
1. Throughput: This is the measure of numbers of packets sent successfully in a network and it is measured in terms of packets per second (Chauchan et al., 2013).
2. ChannelData Rate: Accounts for the total number of user data transmitted by the BS (BS) over the air interface (Araniti et al., 2012).
WiMAX is heterogeneous with unsystematic mix of real and non-real time traffic. The IEEE 802.16 standard provides two modes for sharing the wireless medium (Prasad & Kumar, 2013):
(i) Point-to-Multipoint (PMP)
(ii) Mesh (Optional)
Point-to-Multipoint(PMP)mode:Nodes are arranged in a cellular structure in such a way thatthe BS exchange information with a set of subscriberstations within the same antenna sector in a broadcast mode(Prasad & Kumar, 2013). Here all subscriberstations attain identical transmission from the BS. Where subscriberstations transmissions are targeted to the BSs and get synchronized. Mesh mode: Nodes are arranged systematicallyin Ad-hoc mannerand scheduling is distributed among the subscriber stations without transmission fromthe BS(Prasad & Kumar, 2013). The uplink from subscriber stationto BS and downlink from BSto subscriberstationdata transmissions in the IEEE 802.16 standard are Frame- based (Wei et al., 2005). In recent years, broadband wireless access networks have been rapidly evolving tosatisfy increasing user scalability and QoS. The salient features of IEEE 802.16eas described in (Nithyanandan&Susila, 2013) are:
(i) Higher data rate
(ii) Mobility
(iii) Scalability
(iv) QoS such as optimal system throughput
Figure 1.1 WiMAX Deployment Scenarios (So-In & Tamimi, 2009) In order to support high data rate multicast and broadcast service in WiMAX, Orthogonal Frequency Division Multiplexing (OFDM) technology and its access technique are used. Video, voice and data are all Internet Protocol (IP) data services, but each has its own QoS requirements (Afolabi et al., 2013). High availability, sufficient guaranteed bandwidth, low transmission delay and jitter are the QoS requirements for video services(Nithyanandan&Susila, 2013). In order to support QoS for various types of traffic, WiMAX medium access control protocol defines bandwidth request-allocation mechanism and five types of scheduling classes as follows (Shwetha etal., 2011):
(i) Unsolicited Grant Scheme (UGS)
(ii) real-time Polling Service (rtPS)
(iii) nonreal-time Polling Service (nrtPS)
(iv) Best Effort (BE)
(v) extended real-time Polling Service (ertPS)
The pertinent characteristic of these five scheduling service classes are presented in(Oktay & Mantar, 2013) as follows:
(i) UGS: This scheduling service is designed for periodic fixed-size data packets. It supports constant bit rate real-time applications such as the Voice over Internet Protocol (VoIP) without silence suppression.
(ii) ertPS: This scheduling service class generates periodic variable-sized packets. It supports Variable Bit Rate (VBR) real-time applications such as the VoIP with silence suppression.
(iii) rtPS: This is designed for real-time applications that generate VBRs, such as audio/video (MPEG) streaming and Video on Demand (VoD).
(iv) nrtPS: This service class is designed for delay-tolerant nonreal-time applications such as the File Transfer Protocol (FTP) with guaranteed minimum throughput.
(v) BE: This service class is designed for applications that do not need QoSparameters. The HypertextTransfer Protocol (HTTP) and e-mail are the
examples of this service application. In addition to these scheduling services, mandatory QoSparameters have been designed in the standard WiMAX networks. The scheduling services and supported QoS parameters (IEEE 802.16e,Standard 2009) are shown in Table 1.1
Table 1.1:Mandatory QoS Parameters of the Scheduling Services (Oktay& Mantar,2013)
Services QOS Parameters
UGS T1/E1 transport
ertps VOIP
rtps streaming audio/audio
nrtps FTP
BE HTTP, Data transfer
Tolerated jitter (TJ)

Maximum latency (ML)

Maximum sustained traffic rate (MSTR)

Minimum reserved traffic rate (MRTR)

Traffic priority (TP)

Packet loss


Packet delivery ratio

Table 1.1 depicts the essential QoS parameters of the scheduling services. Here ‗‘ symbol depicts that the service class has QoS defined for that particular parameter, while the ‗x‘ symbol depicts that the service class has no QoS defined for that particular parameter.For real time application like IPTV and VoIP a guaranteed QoS level is very important because these are Constant Bit Rate(CBR)applications and are delay sensitive. However, the IEEE 802.16 standard 2005 that defines these five scheduling classes, does not provide any standard algorithms that should be used to provide QoS to the service flows (Shu‘aibuet al., 2011)
1.2 Statement of problem
The major problem in optimal resource allocation to subscribers of mobile WiMAX that belong to the same multicast group, distributed over different locations are throughput variation (fluctuations) and channel degradation because they experience different fading and path losses in time-varying wireless channels. This presents a challenge to providing satisfactory multicast service to all users within the network range (Tanet al., 2011). In order to improve the system throughput, there is a need to partition the multicast group into smaller sub-groups to allow multiuser diversity to be exploited more efficiently, with a view to maximizing the system throughput and channel data rate. In this research work, a Particle Swarm Optimization Scheduling (PSO) algorithm for multicasting wireless traffic over mobile WiMAX network based on sub-groups formation technique is proposed to improve the system throughput and channel data rate while guaranteeing the QoS for each of the users in the network.
1.3 Aim and Objectives
The aim of this research work is to develop an improved scheduling algorithm for multicast services over WiMAX networks using PSO technique, with a view to maximizing Aggregate Date Rate (ADR) and Channel Data Rate (CDR). The objectives of the research are as follows:
1. To develop a model for WiMAX network environment and throughput, CDR evaluation, as well as an optimal scheduling algorithm for WiMAX multicasting based on improved PSO technique.
2. To simulate various scenarios of WiMAX multicast service to mobile subscribers to maximize ADR and CDR;
3. To validate the performance of the developed model by comparing the results obtained with those of the Maximum Throughput Algorithm (MTA).
1.4 Methodology
The following are the list of the methodology adopted in carrying out this research work:
1) Development of a model for the WiMAX network environment based on seven concentric layers;
2) Development of a model for the WiMAX MobileStations (MSs), such that the sub stations are mobile and the network is configured to change after each frame interval.
3) Determination of an objective function for throughput maximization and CDR based on PSO technique;
4) Development of an improved algorithm for maximizing throughput and CDR, based on items (1) to (3)
5) Simulation of various scenarios of WiMAX multicast service to mobilesubscribers in order to evaluate the ADR and CDR of each scenario.
6) Validation of the developed model by using the parameters published in the work of Araniti et al., (2012).
1.5 Significant Contribution
A lot of researchers have developed various scheduling algorithms on improving the WiMAX system capacity based on maximization of throughput while guaranteeing the desire QoS for each of the users in the network. Therefore, the significant contributions of this research work are itemized in terms of resources utilization as follows:
1. The developed objective function based on PSO technique achieved a maximum ADR and CDR of 88.88% when compared with MTA in terms of ADR and CDR of 80%
2. The developed scheduling algorithm maximizes ADR and CDR in the WiMAX network environment with 8.88% improvement as compared with MTA
1.6 Scope of the Dissertation
The scope of this research work is the development of an improved scheduling algorithm for multicast services over WiMAX network using PSO technique that is limited to maximizing the ADR and CDR of the WiMAX network.
1.7 Dissertation Organization
The general introduction is presented in chapter one, whilethe rest of the chapters are organized as follows: A detailed review of related literatures and relevant fundamental concepts of multicast service, WiMAX, channel propagation model, scheduling algorithms and heuristic techniques arecarried out in chapter two. Comprehensive approach and relevant mathematical models describing the developed objective function and the improved algorithm are presented in chapter three. The performance, analysis and discussions of the results are presented in chapter four. Conclusion and recommendation composes in chapter five. Quoted references and Appendices are also presented at the end of this dissertation.



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