臺灣博碩士論文加值系統

在未來的電信或無線區域網路系統中，對於高傳輸率的下鏈研究，目前多重
使用者正交分頻多工是一個相當熱門的技術。在比例式公平限制下，提高總和容積量則是目前熱門的研究方向。在多重使用者正交分頻多工系統下，欲達到總和容積量最大化，可將每個子通道指派給有最大通道雜訊比的使用者做使用，而接著其傳送必v則依據填充水法則分配。如果考慮到比例公平性的限制，方法[14]可達到總和容積量最大化。在本篇論文中，我們提出了兩種在多使用者正交分頻多工系統的資源分配演算法，目的為滿足不同的比例公平下達到比[14]更高的總和容積量。這個最佳化問題必須在每一次訊號傳送時，同時考慮到最大化總和容積量與維持使用者之間的比例公平性。然而，達到極佳的比例公平性必定會減少系統的總和容積量，這是一個交換的關係。因此，這兩個次最佳化的演算法可以增加總和容積量，但卻不造成過大的公平性偏差值。在我們的演算法中，採用將子通道與傳送必v一起分配。我們使用一個簡單的方法去分配每個使用者的總傳輸必v且易於實現。透過模擬結果可知，所提的兩個演算法，可以達到比方法[14]更高的總和容積量且維持使用者間幾乎一樣的公平性偏差，尤其在高傳輸率要求情況下。我們也秀出所提的演算法，其公平性效率比方法[11]多出三倍，卻可以維持和方法[14]幾乎一樣的使用者比例公平性。此外，我們更提出一種新的演算法，利用放寬使用者之間的公平性偏差臨界值，可以有效的提升系統總和容積率。

Multiuser orthogonal frequency division multiplexing (Multiuser OFDM) is a
promising technique for achieving high downlink capacities in future cellular and wireless local area network (LAN) systems. Achieving high sum capacity under proportional fairness constraints is a popular research topic. The sum capacity of multiuser OFDM is maximized when each subchannel is assigned to the user with the best channel-to-noise ratio for that subchannel, with power subsequently distributed by water-filling. Under proportional fairness constraints, the sum capacity is maximized in [14]. In this paper, we present two resource allocation algorithms in multiuser OFDM systems that achieve higher sum capacity with variable proportional fairness constraints than [14]. The proposed optimization problem considers maximizing the sum capacity while maintaining proportional fairness among users for each channel realization. However, good proportional fairness reduces the sum capacity. There is a tradeoff between sum capacity and fairness deviation. Hence, the proposed suboptimal algorithms are created to increase sum capacity without causing too much fairness deviation. In these suboptimal algorithms, subchannel and power allocation are carried out separately. We use a simple method to determine the total transmit power for each user. This method is easy to implement. Simulation results show that the proposed algorithms achieve higher sum capacity than [14] and keep almost the same proportional fairness among users when data rate requirements are large enough. We show that fairness efficiency of our proposed algorithms is over three times better than [11] while maintaining almost the same proportional fairness among users for high date rate requirements. In addition, we also propose an algorithm which releases fairness deviation among users in order to achieve high capacity.

Table of Contents

Abstract in Chinese I
Abstract in English III
Table of Contents V
List of Figures VII
List of Tables VIII

Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation and Resource Allocation 3
1.3 Objective and Problem Statement 5
1.4 Organization of the Thesis 6
Chapter 2 System Model 8
2.1 System Model 8
2.2 Optimization Problem 11
2.3 Solution of the Optimal Objective Function 12
2.4 Optimal Subchannel Allocation and Power Distribution 15
2.5 Suboptimal Subchannel Allocation and Power Distribution with Proportional Fairness 18
2.6 Fairness Issues 22
Chapter 3 Proposed Suboptimal Subchannel Allocation and Power Distribution Algorithms 24
3.1 Introduction 24
3.2 Two Proposed Algorithms 25
3.2.1 Proposed Algorithm (1): Enhance Sum Capacity with Proportional Fairness (ESCPF) 25
3.2.2 Proposed Algorithm (2): Maximize Sum Capacity with Proportional Fairness (MSCPF) 27
3.3 Numerical Results 28
3.4 Complexity Analysis and Comparison 36
Chapter 4 Fairness Constraints Algorithm 37
4.1 Introduction 37
4.2 The Fairness Constraints Algorithm (FCA) 38
4.3 Numerical Results 39
Chapter 5 Conclusions and Future Works 45
References 47

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