Opportunistic Scheduling for Multicast over Wireless Networks

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郭耀睿(Pavol Polacek) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

(Opportunistic Scheduling for Multicast over Wireless Networks)

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摘要(中)

透過無線傳輸方式的資料量在近十年大幅增長，並且預期在下個世代
對於無線傳輸的需求將是有增無減。為了應對這樣大量增長的需求，提高
無線傳輸的效率和傳輸能力是必然的趨勢近期，Opportunistic Scheduling
(OS)的概念在許多的通訊相關的研究領域被大量探討。OS是在考慮通
道品質的情況下，以機遇式方式對無線通道進行排程來改善無線傳輸時
的效率，排程器會挑選接收訊號品質相對較好的使用者優先傳輸，如此
一來系統的平均吞吐量將會改善許多。論文的主題除了將OS實現在群播
中，同時能應用於Cognitive Radio (CR)與Multimedia Broadcast multicast
service Single Frequency Network (MBSFN)的環境下。探討並克服在上述
兩個情境底下，Opportunistic Multicasting (OM) 排程如何設計才能提升
整體吞吐量。首先，我們探討了Multiuser Diversity (MUD)的理論分析，
並提出統一內部及外部MUD的概念，此概念可以讓我們對系統整體吞吐量
和使用者吞吐量進行公式化，進而在設計CR的演算法時能夠量化吞吐量。
第二，我們針對不同Modulation and Coding Scheme (MCS)的調整間隔，
探討了Signal-to-interference-plus-noise Ratio (SINR)在MBSFN的分布情
形和OM的頻譜效率。運用此結果，我們設計出一低回饋、低複雜度和慢
速調整調變編碼的演算法應用在MBSFN。
綜合以上兩點，我們所設計的演算法，非常適用於CR和在MBSFN的
環境下。大量的模擬結果也顯示我們所提出的演算法比起此前的設計能提
供較佳的服務品質給用戶。

摘要(英)

The demand for wireless data is continuously rising and is expected to
increase. To cope with this growth, it is necessary to work towards higher
network capacity and improved network capabilities.
In the recent period, opportunistic concepts started to take hold and
being part of research into communication technologies. Opportunistic
Scheduling (OS) is a scheduling approach for wireless channels, where channel
quality is considered, when making scheduling decisions. The scheduler
transmits to users with relatively better signal quality, so the average
amount of transmitted data can be increased. The focus of this thesis
will be on OS as a method for increasing the transmission eciency and
its implementation in wireless networks. The main research topic of the
dissertation is thus multicasting with OS, i.e. Opportunistic Multicasting
(OM), and its adaptation to Cognitive Radio (CR) and Multimedia Broadcast
multicast service Single Frequency Network (MBSFN) networks. More
specically, how it ts into the these types of networks and what kind of
obstacles need to be tackled, before OM can be successfully deployed.
First we study the theory of Multiuser Diversity (MUD) and provide
a novel concept unifying internal and external MUD, enabling us to formulate
the system sum and user throughput. The resulting CR algorithm
design approaches the derived throughput advantage. Second, we look
at Signal-to-interference-plus-noise Ratio (SINR) distribution in MBSFN
networks and Spectrum Eciency (SE) of OM under extended Modulation
and Coding Scheme (MCS) adaptation interval. Using the results, we design
a low feedback, low complexity, and slow MCS change based algorithm
suited for MBSFN.
The resulting algorithm designs are well suited to the studied CR
and MBSFN networks. Extensive simulations show, that the presented
algorithm designs perform better than previous designs, enabling better
quality service to users.

關鍵字(中)

★ 感知網路
★ 多媒體廣播

關鍵字(英)

★ WIRELESS NETWORKS
★ COGNITIVE RADIO
★ CHANNEL AWARE SCHEDULING
★ MULTIMEDIA
★ Opportunistic scheduling

論文目次

摘要i
Abstract ii
Acknowledgements iv
Contents vi
List of Figures ix
List of Tables xiii
Acronyms xvi
List of Selected Mathematical Notation xxi
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Wireless Communications: New Opportunities . . . . . . . . 7
1.4 Objectives and Scope of the Thesis . . . . . . . . . . . . . . 9
1.5 Author′s Contributions . . . . . . . . . . . . . . . . . . . . . 11
2 Wireless Channel and Opportunistic Concepts 13
2.1 Wireless Channel . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1.1 Variability of the Wireless Channel . . . . . . . . . . 16
2.2 Mobile Devices . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Theory of Opportunistic Multicasting 21
3.1 Multiuser Diversity . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Unified Opportunistic Scheduling . . . . . . . . . . . . . . . 23
3.2.1 System Model . . . . . . . . . . . . . . . . . . . . . . 23
3.2.2 System Sum Throughput under Unified Opportunistic
Scheduling . . . . . . . . . . . . . . . . . . . . . . 24
3.2.3 Effective User Throughput under Unified Opportunistic
Scheduling . . . . . . . . . . . . . . . . . . . . . . 26
3.3 Opportunistic Scheduling with Extended Adaptation Interval 27
3.3.1 System Model . . . . . . . . . . . . . . . . . . . . . . 28
3.3.2 Opportunistic Multicasting Under Extended MCS
Adaptation Intervals . . . . . . . . . . . . . . . . . . 32
3.3.3 Spectral Efficiency Optimization under Extended Adaptation
Intervals . . . . . . . . . . . . . . . . . . . . . 34
3.3.4 Multi-Stream Transmission . . . . . . . . . . . . . . . 35
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4 Opportunistic Multicasting in CR Networks 39
4.1 Opportunistic spectrum access . . . . . . . . . . . . . . . . . 40
4.1.1 Simulation . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 Opportunistic Scheduling . . . . . . . . . . . . . . . . . . . . 48
4.2.1 iMUD Implementation . . . . . . . . . . . . . . . . . 48
4.2.2 eMUD Implementation . . . . . . . . . . . . . . . . . 49
4.2.3 Simulation . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3 Resource Efficient Fragmentation . . . . . . . . . . . . . . . 56
4.3.1 Simulation . . . . . . . . . . . . . . . . . . . . . . . . 59
4.4 Tile Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.4.1 Simulation . . . . . . . . . . . . . . . . . . . . . . . . 65
4.5 Forward Error Correction . . . . . . . . . . . . . . . . . . . 69
4.5.1 Simulation . . . . . . . . . . . . . . . . . . . . . . . . 72
4.6 Scalable Video Coding . . . . . . . . . . . . . . . . . . . . . 77
4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5 Unified Opportunistic Scheduling Algorithm 85
5.1 The System Model . . . . . . . . . . . . . . . . . . . . . . . 86
5.2 Unified Opportunistic Scheduling Problem . . . . . . . . . . 87
5.3 Unified Opportunistic Scheduling Algorithm . . . . . . . . . 89
5.3.1 Unified Opportunistic Scheduling Algorithm . . . . . 90
5.3.2 Opportunistic Multicasting over CR . . . . . . . . . . 91
5.3.3 Scheduling Priority Adjustment . . . . . . . . . . . . 93
5.3.4 Resource Consumption Monitoring . . . . . . . . . . 94
5.4 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.4.1 Performance with Different Levels of External MUD . 96
5.4.2 UOS Performance . . . . . . . . . . . . . . . . . . . . 97
5.4.3 Performance Under Low Resource Availability . . . 102
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6 Statistical Feedback based Opportunistic Scheduling Algorithm 109
6.1 Statistical Feedback Based Opportunistic Multicast Algorithm
Design . . . . . . . . . . . . . . . . . . . . . . . . . . 110
6.2 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6.2.1 MCS Selection . . . . . . . . . . . . . . . . . . . . . 114
6.3 Adaptation Interval Length . . . . . . . . . . . . . . . . . . 115
6.3.1 Single Video Performance . . . . . . . . . . . . . . . 116
6.3.2 Multiple Video Transmission . . . . . . . . . . . . . . 118
6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
7 Conclusion 121

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指導教授

黃志煒

審核日期

2016-7-14

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