臺灣博碩士論文加值系統

許多無線通訊技術已被廣泛應用在無線網路中，例如無線區域網路(Wireless Local Area Network, WLAN)、全球互通微波存取(Worldwide Interoperability for Microwave Access , WiMAX)以及長程演進技術(Long Term Evolution, LTE)，長程演進技術進階(LTE-Advanced)等。行動用戶可在異質無線網絡（Heterogeneous Wireless Network, HWN）中透過調變技術、換手交接以及資源管理等進行無縫接軌的通訊。本論文中行動用戶可以從一個無線網路切換到另一個，而換手交接的時機是基於多種屬性和用戶的偏好。無線網路支援各種越區交換的機制，已有一些呼叫管理策略被用來實現異質網路的無縫切換;然而這些決策方法仍存在諸多缺點有待克服。本論文採用的智慧型演算法具多屬性決策且具備風險迴避預測機制。行動用戶基地台可動態選擇最佳網路， 網路供應商則須提供具服務品質（Quality of Service, QoS）和最優的資源配置給異質網路用戶。但是在異質無線網路中之研究議題仍然存在許多的挑戰, 例如資源分配，資源管理和排程等工作仍待提高服務品質。然而，在WiMAX標準中對最佳時槽(time slot)分配並無確切定義。在本論文中，一個新的結合灰色關聯分析和背包演算法（Grey Relational Analysis and Knapsack Algorithm, GRAKA）且具多屬性特色的上傳頻寬分配機制，被提出來用以提昇資源使用率及吞吐量，同時滿足公平性需求。在過去的幾年中，傳統結構的協定(protocol)只能做到單一層的最佳化，卻無法在無線網路環境中的跨層(cross layer)的資源分配中進行最佳化。本論文應用智慧型演算法，多屬性決策（Multiple Attributes Decision Making, MADM），預測演算法，跨層資源分配之排程設計，資訊流量分類，資源管理引擎和服務品質等組件來處理跨層多媒體資源管理和排程。另外本論文提出的可調適性灰關聯理論分析(Adaptive Grey Relational Analysis, AGRA)演算法可以顯著降低呼叫阻塞時間與阻絕率，並穩定其連通率，因而提供更高的服務品質。除外我們所提出之高效能的下載資源多階層式排程演算法整合了六種資料傳輸型態，模擬結果顯示此演算法可以降低特定型態資料傳輸延遲，且提升整體資料傳輸服務品質及整體資料傳輸量。最後本論文所提出的高效能上傳資源排程演算法，使用了多屬性灰色關聯分析和背包算法（Grey Relational Analysis Algorithm with Multiple Attributes and Knapsack Algorithm , GRAMAKA），實驗結果顯示此方法在吞吐量和公平性指標的績效優於模糊邏輯演算法及傳統背包演算法。

Various wireless technologies are rapidly developed for the Next Generation Wireless access Networks (NGWN), such as WLAN, WiMAX, LTE, LTE-Advanced which are supposed to support universal multi-media service for mobile users. In the future, the mobile users are expected to communicate with each other in the Heterogeneous Wireless Network (HWN) with different modulation capability. In heterogeneous wireless networks, a mobile set may access the network from different services providers through various techniques such as handover, resource management, etc. The handover decision is based on multiple attributes and user preferences. Wireless networks support different handoff mechanisms, through which the mobile set can switch from one wireless network to another. Several call management strategies have been proposed to realize seamless handoff in heterogeneous networks; however, these decision-making methods appear arbitrary and, in some cases, even debatable. This study proposed using intelligent algorithm with multiple attributes and prediction-based risk-avoiding mechanism for handover decisions. The mobile set dynamically selects the optimal network and provides the user with high Quality of Service (QoS) and optimal resource allocation in heterogeneous networks. However, there are many challenges for HWN to provide a high QoS with different modulations, such as resource allocation, resource management, and scheduling. However, no WiMAX standards for optimal slot allocation have been defined. In this study, a new uplink bandwidth allocation scheme Grey Relational Analysis Knapsack Algorithm (GRAKA) which combines grey relational analysis with multiple attributes and the knapsack algorithms is proposed to improve resource utilization while satisfying the user’s requirements of throughput and fairness. In the past few years, the traditional architectures protocols are only optimized in each layer. Thus, the performance of analysis for cannot be optimized in the management about resource allocation for cross layers in the wireless network environment. The framework is designed which includes intelligent algorithms, Multiple Attributes Decision Making (MADM), prediction algorithms, cross layer resource allocation scheduling design, traffic classifier, resource management engine and QoS components to handle the cross layer architecture with multimedia service resource management and scheduling for supporting high QoS over heterogeneous wireless network in the 3GPP LTE networks. The intelligent scheduling algorithms are proposed to improve the system throughput and QoS. In this thesis, proposed algorithm significantly lowers call blocking times and lowers dropping rate with stable connections and allows higher quality of service. In this thesis, indicate that the proposed downlink resource management, HPWRR scheduling algorithm for six different traffic profiles particularly the integration of multicast traffic, and efficient scheduling of the traffic types. The simulation results show that the proposed schemes effectively reduce the average delay time of the specified traffic types and improve the quality of service and total throughput. In this thesis, indicate that the proposed uplink resource management, Grey Relational Analysis with Multiple Attributes and Knapsack Algorithm (GRAMAKA) performs best in terms of performance index which consists of throughput and fairness index.

Abstract...............................................v
誌 謝................................................vii
Contents..............................................viii
List of tables ......................................x
List of figures ......................................xi
Chapter 1 Introduction...........................1
1.1 Physical layer description.....................4
1.2 OFDMA symbol structure and sub-channelization..5
1.3 Scalable OFDMA.................................6
1.4 TDD frame structure............................7
1.5 Other advanced PHY layer features..............8
1.6 Research Background............................10
1.7 Research Motivation............................12
1.8 Scope of work..................................13
1.9 Organization of this dissertation..............14
Chapter 2 Related works..........................16
2.1 Grey Relational Analysis (GRA).................16
2.2 Call Admission Control Management strategy.....17
2.3 The cross layer concept 22
Chapter 3 Call control management for heterogeneous wireless networks......................................25
3.1 Integrated ISV call management strategy........25
3.1.1 Media Independent Handover (MIH)...............27
3.1.2 Proposed call management strategy..............28
3.1.3 System performance analysis....................36
3.2 CAC with multiple attributes decision making (MADM) ...............................................44
3.2.1 Concept of Grey Relational Analysis (GRA)......47
3.2.2 CAC management strategy with MADM..............48
3.2.3 System performance analysis....................50
3.3 Adaptive GRA using MADM with risk management...54
3.3.1 Risk management with MADM......................55
3.3.2 AGRA call management algorithm.................56
3.3.3 System performance analysis....................59
3.4 Summary........................................64
Chapter 4 Bandwidth allocation for WiMAX network.65
4.1 Downlink resource management...................65
4.1.1 Scheduling for downlink in WiMAX network.......68
4.1.2 Proposed HPWRR scheduling......................72
4.1.3 System performance analysis....................74
Chapter 5 Uplink resource management.............82
5.1 Scheduling for uplink in WiMAX network.........83
5.2 Intelligent algorithms.........................85
5.3 Proposed GRAMAKA algorithm.....................88
5.3.1 System performance analysis....................91
5.3.2 Summary........................................100
Chapter 6 Conclusions and future works...........102
6.1 Conclusion.....................................102
6.2 Direction for future research..................104
[References]...........................................105

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