臺灣博碩士論文加值系統

本論文主要針對在正交分頻多工多媒體行動通訊網路的議題中，提出有效的資源分配機制。這些議題分別為：通道狀態訊息回報超額量之降低、最大化系統輸出與保證服務品質兩大效能取捨之平衡、以及多細胞環境中細胞間干擾影響之減輕。
我們首先提出可運作於部份通道狀態回報環境之資源分配機制，即通道狀態回報節約機會式排程機制；該機制包含服務品質保證排程演算法，與通道狀態回報超額量降低演算法。服務品質保證排程演算法依照緊急程度，給予用戶不同的優先權，並設定優先權臨界值，決定用戶需依據其優先權或通道狀態進行排程，以滿足用戶服務品質需求並最大化系統輸出量；通道狀態回報超額量降低演算法則依據用戶的緊急程度預測下一個訊框可能會被服務到之用戶，並僅允許這些用戶回報其通道狀態以減少回報超額量。與其他傳統資源分配機制比較，我們提出的通道狀態回報節約機會式排程法不但大量降低通道狀態回報超額量，還能達到較高的系統輸出並同時滿足用戶服務品質需求。
我們延續論文第一部份的成果，探討了如何將無線資源分配在最大化系統輸出量與保證服務品質兩者間取得平衡。這個議題主要是因為在提供服務品質保證考量下，資源則必須分配給較緊急之用戶而非通道狀態較好之用戶，因此會造成系統輸出量的損失。為了達到一個好的資源分配平衡，我們提出了動態優先權臨界值平衡資源排程機制，這個機制包含了優先權導向資源分配演算法與通道狀態導向資源分配演算法。首先，用戶會根據其優先權與相對應之優先權臨界值，依序被優先權導向資源分配演算法與通道狀態導向資源分配演算法所服務。藉由調整這兩種資源分配演算法所服務之用戶數，我們可以控制系統輸出量最大化與服務品質保證兩者間的平衡。其間最關鍵的是，我們所提出的乏晰推論型優先權臨界值產生器來動態地與智慧地產生優先權臨界值，以達成好的平衡。模擬結果顯示，藉由避免過度滿足用戶之服務品質需求，我們提出的平衡資源排程機制比傳統的利益函數型與其他資源分配機制，能有更高的系統輸出，同時也能保證用戶的服務品質。
最後，我們進一步探討在多細胞環境下細胞間干擾之資源分配問題。為了在細胞間干擾環境下提供高系統輸出並保證用戶服務品質，我們必需保證各用戶的服務機會，也就是說各用戶的通道狀態必需能夠支援最小資料傳輸量；因此我們提出了多重傳輸點部份頻率重用策略與多重傳輸點協調資源分配機制。多重傳輸點部份頻率重用策略藉由多重傳輸點佈置，來降低細胞邊界用戶的傳輸路徑損失，接著透過增強型部份頻率重用，來減輕細胞間干擾之影響並提升頻譜效率。雖然多重傳輸點解決路徑損失之影響，也帶來了細胞內傳輸點相互干擾之問題。藉由精心設計之利益函數，多重傳輸點協調資源分配機制以降低細胞內干擾、最大化系統輸出、與保證用戶服務品質為目標，將資源與傳輸點分配給用戶，同時考慮各傳輸點間採用聯合傳送或干擾必免之合作方式。模擬結果驗證了多重傳輸點部份頻率重用策略搭配多重傳輸點協調資源分配機制能有效降低傳輸路徑損失與細胞間干擾對邊界用戶之影響，保證各用戶之服務機會，因此能提供比其他傳統資源分配機制更高的系統輸出，並且在多細胞環境下提供服務品質保證。

This dissertation is aimed at resource allocation issues in multimedia orthogonal frequency division multiple access (OFDMA) mobile communication networks, where the issues include channel-state-information (CSI) overhead reduction, balance between system throughput maximization and quality-of-service (QoS) guarantee, and inter-cell interference (ICI) alleviation of multicell environment.
First, we propose an economized-CSI opportunistic scheduling (ECOS) scheme. The ECOS scheme consists of a quality-of-service (QoS) guarantee scheduling (QGS) algorithm and a CSI overhead reduction (COR) algorithm. The QGS algorithm fulfills QoS requirements by dynamic priority value adjustment, while the COR algorithm reduces the amount of CSI overhead by limiting the number of feedback users. Simulation results show that the proposed ECOS scheme greatly reduces
the uplink bandwidth occupancy of CSI feedback. Also, it achieves high system throughput and maintains QoS guarantee at high traffic load.
Second, we propose a balanced resource scheduling (BRS) scheme to balance tradeoff between QoS requirement guarantee and system throughput maximization. Based on the adaptive priority threshold of each user, the BRS scheme schedules users by a priority-based resource allocation algorithm and a CSI-based resource allocation algorithm to guarantee QoS and enhance throughput, respectively. Most
important, we propose a fuzzy inference priority threshold generator (FIPG) to adaptively and intelligently adjust the priority thresholds to strike the excellent balance. Simulation results show that the proposed BRS scheme with adaptive priority threshold achieves higher system throughput than conventional resource allocation schemes under a QoS requirement guarantee.
Finally, we propose a multiple-point fractional frequency reuse (MFFR) strategy and a multiple-point coordination resource allocation (MCRA) scheme to improve system throughput and guarantee QoS in multicell environment. The MFFR strategy employs a multiple-point deployment and an enhanced FFR to effectively overcome the pathloss and alleviate the inter-cell interference (ICI), respectively,
while the MCRA scheme is developed to solve the intra-cell interference brought by the MFFR strategy, maximize system throughput, and fulfill QoS requirements by coordinating multiple transmission points. Simulation results show that the MCRA scheme can attain higher system throughput and cell edge user throughput than the
conventional resource allocation schemes. More important, by guaranteeing service opportunity of users, the MCRA scheme is capable to satisfy QoS requirements of real-time and non-real-time users in the multicell environment.

Mandarin Abstract i
English Abstract iii
Acknowledgements v
Contents vi
List of Figures x
List of Tables xii
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Paper Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . . 10
2 An Economized-CSI Opportunistic Scheduling Scheme for OFDMA/
TDD Downlink Systems 13
2.1 Introduction . .. . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.1 System Configuration . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.2 QoS Requirements of Multimedia Traffics . . . . . . . . . . . 16
2.2.3 CSI Quantization . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Economized-CSI Opportunistic Scheduling (ECOS) Scheme . . . . . 18
2.3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.2 QoS Guaranteed Scheduling (QGS) Algorithm . . . . . . . . . 21
2.3.3 CSI Overhead Reduced (COR) Algorithm . . . . . . . . . . . 25
2.3.4 Feedback Channel Assignment . . . . . .. . . . . . .. . . . 29
2.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . .30
2.4.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 A Balanced Resource Scheduling Scheme with Adaptive Priority
Thresholds for OFDMA Downlink Systems . . . . . . . . . . . . . . 39
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2.1 System Configuration . . . . . . . . . . . . . . . . . . . . . . . 42
3.2.2 Multimedia Service Traffic . . . . . . . . . . . . . . . . . . . . 42
3.2.3 Channel State Information . . . . . . . . . . . . . . . . . . . . 43
3.3 Balanced Resource Scheduling (BRS) Scheme . . . . . . . . . . . . . 45
3.3.1 Priority Value . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.2 Assigned Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3.3 Priority-based and CSI-based RA Algorithms . . . . . . . . . 50
3.4 Fuzzy Inference Priority-Threshold Generator (FIPG) . . . . . . . . . 53
3.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.5.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . 59
3.5.2 Traffic Model and QoS Requirement . . . . . . . . . . . . . 59
3.5.3 Compared Resource Allocation Schemes . . . . . . . . . . . . 61
3.5.4 Performance Evaluation of Case I . . . . . . . . . . . . . . . . 63
3.5.5 Performance Evaluation of Case II . . . . . . . . . . . . . . 68
3.5.6 Computation Complexity Analysis . . . . . . . . . . . . . . . 73
3.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4 A Multiple-Point FFR Strategy with Multiple-Point Coordination
Resource Allocation Scheme for Multicell OFDMA Downlink Systems
75
4.1 Introduction . . . . . . . . .. . . . . . . . . . . . . . . . 75
4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2.1 System Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2.2 Multimedia Services and Traffics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.3 Multiple-Point Fractional Frequency Reuse
(MFFR) Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.4 Utility Function Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85
4.4.1 QoS Status Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87
4.4.2 Subchannel Allocation Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .88
4.4.3 Efficient Assignment Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.4.4 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.5 Multiple-Point Coordination Resource Allocation (MCRA) Scheme . 92
4.5.1 Expected Resource Amount . . . . . . . . . . . . . . . . . . 93
4.5.2 Coordinated Point Selection (CPS) Algorithm . . . . . . . . . 96
4.5.3 Maximum Utility Allocation (MUA) Algorithm . . . . . . . . 97
4.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.6.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . 98
4.6.2 Traffic Model and QoS Requirement . . . . . . . . . . . . . 101
4.6.3 Compared Schemes . . . . . . . . . . . . . . . . . . . . . . . . 102
4.6.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 104
4.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
5 Conclusions and Future Works . . . . . . . . . . . . . . . .117
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . .121
Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . .132

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