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系統識別號 U0002-1006200511454200
中文論文名稱 媒介存取控制層上智慧型訊框傳送策略之研究
英文論文名稱 A Study on Intelligent Frame Transmission Strategies in Medium Access Control (MAC) Layer
校院名稱 淡江大學
系所名稱(中) 電機工程學系博士班
系所名稱(英) Department of Electrical Engineering
學年度 93
學期 2
出版年 94
研究生中文姓名 莊岳儒
研究生英文姓名 Yue-Ru Chuang
學號 888350021
學位類別 博士
語文別 英文
口試日期 2005-05-23
論文頁數 94頁
口試委員 指導教授-許獻聰
委員-黃仁竑
委員-黃俊堯
委員-陳彥文
委員-李揚漢
中文關鍵字 基因演算法  多媒體應用  管線化  服務品質 
英文關鍵字 Genetic algorithms (GAs)  Multimedia applications  Pipeline  Quality of service (QoS) 
學科別分類
中文摘要 為了降低通訊協定設計之複雜度與提升管理維護之方便性,大部分的網路通訊協定都採用分層式的架構,亦即由一系列階層式 (layered) 的協定所組成。各階層通訊協定提供不同的服務,再將此階層協定一層層的疊起來以實現完整的網路通訊服務。在國際標準組織 (international standards organization – ISO) 所提出的開放性系統互連 (open systems interconnection – OSI) 參考模型中,電腦網路通訊協定被分成七個階層。而本論文乃針對媒介存取控制層 (medium access control – MAC) 通訊協定進行研究進而提出智慧且有效率的資料傳送策略。此類策略乃是藉由避免或減少資料在傳送時因共用傳輸媒介而造成的競爭或碰撞之情況,來更進一步提升有線網路與無線網路的傳輸效能。
論文中第一個提出的策略是一種中央控制式的高速排程機制。此中央控制式的控制器執行一個改良型的基因演算法 (generic algorithm – GA) 以快速地對儲存在各個使用者端 (station – STA) 的資料封包 (packet) 進行傳送順序的排程 (schedule)。基因演算法常常被用來處理複雜的問題以求得最佳解,但其求解的速度緩慢。因此,我們改良傳統的基因演算法提出一種超世代基因演算法 (hyper-generation GA – HG-GA),並將其應用在中央控制式的高速排程機制中。此超世代基因演算法打破傳統基因演算法以「世代」 (generation) 為進化單位之原則,同時採用高度管線化 (pipeline) 技術以加速取得最佳排程結果所需之收斂時間。考量在一個高密度波長分工多工 (dense wavelength division multiplexing – DWDM) 的高速光網路環境下執行所提出具超世代基因演算法之中央控制器,數學分析與模擬結果皆顯示在相同的進化時間內,超世代基因演算法比傳統基因演算法能產生更多且更好的染色體 (chromosomes) (即指排程之結果)。根據有限時間內所計算得出封包排程結果將有助於提升光網路的傳輸效能。
論文所提出的第二個策略是一種無線網路分散控制式的機制。此策略提出一種具繼承性的訊標 (token)。此繼承訊標被包含於傳送的資料訊框 (frame) 中,凡是收到此繼承訊標之使用者端皆能立即傳送資料訊框而無需再參與傳輸通道的競爭。我們稱此策略為M次繼承之傳輸策略 (M-time inheriting transmission strategy – MITS)。所提之策略可容易地被應用於具基礎架構 (infrastructure) 或隨意架構 (ad hoc) 之無線區域網路中。由於IEEE 802.11無線區域網路技術的發展,使無線網路所提供之頻寬已逐漸滿足多媒體應用之要求。因此,在無線網路中需設計一種「動態雙向的傳輸協定」以能有效地處理某些互動型的多媒體應用。此M次繼承傳輸策略可以藉由減緩使用者端傳輸競爭之程度與降低頻繁的後退處理程序 (backoff process) 來有效地提升系統的傳輸效能 (如產量 (goodput))。模擬結果顯示在相同的通道容量條件下,此傳輸策略確實比標準的IEEE 802.11 MAC協定能達到更好的表現。
論文最後提出一適用於無線網路中央控制式的機制。在有基礎架構之無線區域網路中擷取點 (access point – AP) 藉由延後回覆ACK訊框給發送端,以控制並排程這些使用者端的資料傳送順序。此策略稱之為以ACK為基礎之輪詢策略 (ACK-based polling strategy – APS)。無線網路擷取點能夠延遲那些使用者端其暫存器中仍有待傳送資料的ACK訊框,以達到暫時地終止它們對網路的存取競爭。一個被控制的使用者端只有當收到由擷取點所回覆之ACK訊框時,才算是被通知取得傳送資料訊框之權力。此輪詢策略降低通道競爭的花費 (overhead) 與頻繁的交握程序 (handshaking) 進而提升網路頻寬的有效使用率。此外,此策略被進一步地改良來支援各類多媒體應用所需之服務品質 (quality of service – QoS)。模擬結果顯示此具有增強服務品質功能的輪詢策略能夠滿足多媒體無線網路中的各種傳輸需求。
英文摘要 Most of the network communication protocols adopt layered architectures to reduce the complexity of protocol design. In general, the transmission efficiency of communication protocol is affected by the design of the medium access control (MAC) layer. This thesis mainly focuses on the designs of intelligent and effective frame transmission strategies used in the MAC layer of either wired or wireless networks. A centralized control scheme is first proposed for scheduling the sequence of frame transmissions of stations (STAs) in high-speed dense-wavelength-division-multiplexing (DWDM) optical networks. The proposed controller executes an enhanced genetic algorithm (GA), named hyper-generation GA (HG-GA), to quickly obtain the near optimal transmission sequence of data frames that are queued in STAs. On the other hand, two frame transmission strategies are proposed to improve the transmission efficiency of wireless networks. One strategy, named M-time inheriting transmission strategy (MITS), allows the sender to use the ‘inheritance token’, which is carried on the transmitted frame, to reserve channel access right for the recipient. The other strategy, named ACK-based polling strategy (APS), is proposed to control the sequence of frame transmissions via deferring the acknowledgement (ACK) frames from access point (AP). Both MITS and APS alleviate the degree of transmission contentions among wireless STAs and, consequently, the transmission efficiency of wireless networks are improved. In addition, their amendment versions are designed to fully support the quality of service (QoS) requirements of multimedia applications. Analysis and simulation results demonstrate that three proposed frame transmission strategies for the wired and wireless MAC protocols significantly improve the network throughput.
論文目次 Chinese Abstract •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• I
English Abstract ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• II
Table of Contents ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• III
List of Figures ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• IV
List of Tables ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• V
Chapter 1: Introduction •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 1
1.1 Preface •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 1
1.2 Study Motives ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 3
1.2.1 The Pipeline-based Genetic Algorithm Accelerator ••••••••••••• 4
1.2.2 The M-time Inheriting Transmission Strategy (MITS) •••••• 6
1.2.3 The ACK-based Polling Strategy (APS) ••••••••••••••••••••••••••••••••••••• 8
1.3 Thesis Organization •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 10
Chapter 2: A Pipeline-based Genetic Algorithm Accelerator
for Packet Scheduling and Channel Assignment ••••••••••• 11
2.1 Introduction ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 11
2.2 Hyper-generation Genetic Algorithm (HG-GA) ••••••••••••••••••••••••••••••••••• 11
2.2.1 A Case Study •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 12
2.2.2 Time Domain Analysis of System Throughput ••••••••••••••••••••• 14
2.2.3 Example Illustration •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 17
2.2.4 Saturated Population Size of a Group in the HG-GA ••••••• 19
2.3 Hardware Design of the HG-GA on a Real-time System •••••••••••••••• 20
2.3.1 Problem Description •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 20
2.3.2 Parameter Definition ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 21
2.3.3 GA Operations •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 21
2.3.4 Hardware Block Diagram of the HG-GA Packet
Scheduler ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 23
2.4 Statistical Analyses of Convergence Property ••••••••••••••••••••••••••••••••••••••• 24
2.4.1 C-GAPS •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 25
2.4.2 HG-GAPS •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 28
2.5 Simulation Model and Results ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 33
2.5.1 Simulation Model ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 33
2.5.2 Simulation Results •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 33
Chapter 3: Adopting M-time Inheriting Transmission Strategy
for Interactive Multimedia Wireless Networks •••••••••••••• 37
3.1 Introduction ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 37
3.2 M-time Inheriting Transmission Strategy (MITS) ••••••••••••••••••••••••••••••• 37
3.2.1 One-time Transmitting Inheritance •••••••••••••••••••••••••••••••••••••••••••••• 38
3.2.2 M-time Transmitting Inheritance ••••••••••••••••••••••••••••••••••••••••••••••••••• 40
3.3 Active Invitation Strategy (AIS) ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 41
3.4 System Goodput Analyses of the Standard and the MITS
MAC Protocols ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 43
3.4.1 The Standard MAC Protocol ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 43
3.4.2 Analytical Results of the Standard MAC Protocol ••••••••••••• 47
3.4.3 The MITS MAC Protocol •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 49
3.4.4 Analytical Results of the MITS MAC Protocol ••••••••••••••••••• 54
3.5 Simulation Model and Results ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 56
3.5.1 Simulation Model ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 57
3.5.2 Simulation Results •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 58
Chapter 4: An Ack-based Polling Strategy for Supporting
QoS in Wireless Networks ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 62
4.1 Introduction ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 62
4.2 The ACK-based Polling Strategy (APS) •••••••••••••••••••••••••••••••••••••••••••••••••••• 62
4.2.1 The APS Superframe •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 63
4.2.2 The Restriction of Deferred ACK Frame ••••••••••••••••••••••••••••••••• 66
4.2.3 The ACK Identification Problem (AIP) •••••••••••••••••••••••••••••••••••• 67
4.3 The Enhanced APS for Supporting QoS •••••••••••••••••••••••••••••••••••••••••••••••••••• 67
4.3.1 Scheduling Algorithm •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 69
4.3.2 The Fake-ACK Mechanism •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 71
4.4 System Goodput Analyses of the APS MAC Protocols ••••••••••••••••••• 72
4.5 Simulation Model and Results ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 80
4.5.1 Simulation Model and QoS Definitions •••••••••••••••••••••••••••••••••••• 80
4.5.2 Analyses Verification •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 81
4.5.3 Simulation Results •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 82
Chapter 5: Conclusion •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 86
References •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 88
Biographical Sketch ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 92
Publications ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 93

LIST OF FIGURES
Figure 1.1 OSI reference model. ···································································································· 2
Figure 1.2 The comparison between OSI and TCP/IP reference models. ························· 2
Figure 2.1 A flow diagram of the HG-GA. ·············································································· 12
Figure 2.2 An example of the concepts of groups and the hyper-generation operation
in the HG-GA, where the base generation contains ten parent chromosomes.
············································································································································ 13
Figure 2.3 An example of offspring generation time of the HG-GA, where N=10,
x=2, y=1 and z=2. ········································································································ 18
Figure 2.4 An example of the packet scheduling problem in a star-based network. ··· 20
Figure 2.5 Executing crossover and mutation operations for the packet scheduling
problem: (a) the crossover operation, (b) the mutation operation, (c) the
generated offspring with a small TRST. ······························································· 22
Figure 2.6 A hardware architecture of the HG-GAPS. ························································· 24
Figure 2.7 The analysis results of the convergence properties of the C-GAPS and the
HG-GAPS under different mean packet lengths (L). ········································ 32
Figure 2.8 The simulation results of the HG-GAPS, C-GAPS, and SS-GAPS, where
the simulated parameters are K=8, Λ=8, and L=5: (a) the average TRST,
(b) the bandwidth utilization, (c) the accumulated chromosomes. ··············· 34
Figure 2.9 The comparisons of TRSTs obtained from the C-GAPS, SS-GAPS and
HG-GAPS for ten consecutive scheduling windows. ······································· 36
Figure 3.1 An example of data transmission using the MITS (M=1) in ad hoc
architecture. ··················································································································· 38
Figure 3.2 Using control frames for each data transmission to solve the hidden
terminal problem (here M=1). ·················································································· 39
Figure 3.3 An example of data transmission using the MITS (M=1) in infrastructure
architecture. ··················································································································· 40
Figure 3.4 Two examples of data transmission using the MITS (M=3) in an ad hoc
environment. ················································································································· 41
Figure 3.5 Using the AIS to elucidate the collision process in infrastructure
IV
architecture. ··················································································································· 42
Figure 3.6 Statistical model of the standard MAC protocol. ·············································· 45
Figure 3.7 Displaying fifteen repeated transmission periods (15 run times) to
illustrate the different goodput values, where the total traffic loads are
2.2, 5.5 and 11Mbps. ·································································································· 49
Figure 3.8 Displaying different goodput values obtained from analysis and
simulation, where the mean frame lengths are 500 and 1500bytes. ············· 50
Figure 3.9 Statistical model of the MITS MAC protocol. ··················································· 50
Figure 3.10 Displaying fifteen repeated transmission periods (15 run times) to
illustrate the different goodput values, where the total traffic loads are
2.2, 5.5 and 11Mbps. ·································································································· 55
Figure 3.11 Presenting the system goodput obtained from analysis and simulation,
where the data frame lengths are 500 and 1500bytes. ······································ 55
Figure 3.12 Presenting the system goodput obtained from analysis and simulation to
display the effect of inheritance times, where the total traffic loads are
2.2, 5.5 and 11Mbps. ·································································································· 56
Figure 3.13 Displaying the system goodput to investigate the effect of inheritance
times, where the total traffic loads are 2.2, 5.5 and 11Mbps and the mean
frame length is 825bytes. ·························································································· 58
Figure 3.14 Presenting the effect of the AIS on system goodput, where the total traffic
loads are between 1.1 and 11Mbps and the mean frame length is 825bytes.
············································································································································ 59
Figure 3.15 Displaying average MAC delays of STA and AP to illustrate the effect of
inheritance times in the ad hoc and infrastructure environments, where the
total traffic loads are 5.5 and 11Mbps and the mean frame length is
825bytes. ························································································································ 60
Figure 3.16 Displaying average MAC delays of STA to illustrate the effect of AIS in
the infrastructure environment, where the total traffic loads are between
1.1 and 11Mbps and the mean frame length is 825bytes. ······························· 60
Figure 4.1 An example of the ACK-based polling strategy (APS). ·································· 65
Figure 4.2 An example of AP/STA handling the ACK/Data frames with and without timeout. ··························································································································· 67
Figure 4.3 Numbering ACK frames to solve the ACK identification problem (AIP):
(a) without a sequence bit, (b) with a sequence bit. ·········································· 68
Figure 4.4 The structure of scheduling mechanism supporting QoS services in AP. ·· 69
Figure 4.5 Two scheduling examples of the QAPS: (a) a proper schedule, (b) an
improper schedule. ······································································································ 71
Figure 4.6 A comparison of the QAPS without/with the fake-ACK mechanism (the
top/down one). ·············································································································· 72
Figure 4.7 An analysis model of the APS MAC protocol under the statistical
consideration. ················································································································ 73
Figure 4.8 The comparisons of system goodput obtained by analysis and simulation,
where the data frame lengths (L) are 64, 500 and 1500bytes. ······················· 82
Figure 4.9 The comparisons of system goodput obtained by the APS, the RIMA-DP
and the standard MAC protocol, where the mean frame lengths are
1500bytes and 64bytes. ······························································································ 82
Figure 4.10 Presenting the system goodput and goodput improvement ratio of the
APS and the standard MAC protocol under different mean frame lengths
and traffic loads: (a) the system goodput, (b) the goodput improvement
ratio. ································································································································· 83
Figure 4.11 Comparisons of the average access delay obtained by the QAPS and the
standard MAC protocol, where the simulated traffic types are voice, video,
multimedia and background: (a) the average access delay of the QAPS, (b)
the average access delay of the standard MAC protocol. ································ 85

LIST OF TABLES
Table 2.1 Relationship between the population sizes N of general GAs and the
G(SAT) of the HG-GA. ·································································································· 19
Table 2.2 An example of the origin classification of groups. ················································ 28
Table 3.1 Analytical collision probabilities Pck(wi), where the backoff window size wi
is between 32 α and 1024 α and the number of contending frames k is from 1
to 10. ····································································································································· 44
Table 3.2 Relationship between the numbers of contending frames (CF) and average
collision times (CT) obtained from statistical analysis and simulation. ·········· 48
Table 3.3 Relationship between the numbers of contending frames (CF) and average
backoff interval (BI) obtained from statistical analysis and simulation. ········· 48
Table 3.4 System parameters of the simulation model. ··························································· 57
Table 4.1 Four traffic types in the simulation model. ······························································ 80
Table 4.2 Some system parameters of the simulation model. ················································ 81
Table 4.3 Four traffic types and their required bandwidths in a simulation model. ······· 84
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