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研究生:徐文壕
研究生(外文):Wun-Hao Syu
論文名稱:在多使用者正交分頻多工系統之公平限制下最大化傳送容量
論文名稱(外文):Maximizing Sum Capacity with Fairness Constraints in Multiuser OFDM Systems
指導教授:溫志宏溫志宏引用關係
指導教授(外文):Jyh-Horng Wen
學位類別:碩士
校院名稱:國立中正大學
系所名稱:通訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:59
中文關鍵詞:多使用者正交分頻多工傳送容量
外文關鍵詞:Sum CapacityMultiuser OFDM Systems
相關次數:
  • 被引用被引用:1
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  • 收藏至我的研究室書目清單書目收藏:1
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
References

[1].J. A. C. Bingham, “Multicarrier modulation for data transmission: an idea whose time has come,” IEEE Communications Magazine, vol. 28, no. 5, pp. 5–14, May 1990.
[2].L. Jr. Cimini, “Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing,” IEEE Transactions on Communications, vol. 33, no. 7, pp. 665–675, July 1985.
[3].J. Jang and K. B. Lee, “Effects of frequency offset on MC/CDMA system performance,” IEEE Communications Letters, vol. 3, no. 7, pp. 196–198, July 1999.
[4].H. Sampath, S. Talwar, J. Tellado, V. Erceg and A. Paulraj, “A fourth-generation MIMO-OFDM broadband wireless system: design, performance and field trial results,” IEEE Communications Magazine, vol. 40, no. 9, pp. 143–149, Sep. 2002.
[5].T. S. Rappaport, A. Annamalai, R. M. Buehrer and W. H. Tranter, “Wireless communications: past events and a future perspective,” IEEE Communications Magazine, vol. 40, no. 5, Part Anniversary, pp. 148–161, May 2002.
[6].Adaptix, “Software defined core fast switching-OFDMATM–TDD operating system for next generation mobile broadband wireless networks,” December 21, 2004.
[7].Alcatel, “WiMAX, making ubiquitous high-speed data services a reality,” Strategy White Paper.
[8].L. Atzori, D. D. Giusto and M. Murroni, “Performance analysis of fractal modulation transmission over fast-fading wireless channels,” IEEE Transactions on Broadcasting, vol. 48, no. 2, pp. 103–110, June 2002.
[9].E. Lawrey, “Multiuser OFDM,” ISSPA ’99. Proceedings of the Fifth International Symposium on Signal Proceeding and Its Applications, vol. 2, pp. 761-764, Aug. 1999.
[10].C. Y. Wong, R. S. Cheng, K. B. Lataief, and R. D. Murch, “Multicarrier OFDM with adaptive subcarrier, bit and power allocation,” IEEE Journal on Selected Areas in Communications ,vol. 17, no. 10, pp. 1747–1758, Oct. 1999.
[11].J. Jang and K. B. Lee, “Transmit power adaptation for multiuser OFDM systems,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 2, pp. 171–178, Feb. 2003.
[12].W. Rhee and J. M. Cioffi, “Increase in capacity of multiuser OFDM system using dynamic subchannel allocation,” IEEE 51st Vehicular Technology Conference Proceedings, VTC 2000-Spring Tokyo, Japan, vol. 2, pp. 1085-1089, May 2000.
[13].I. Kim, H. L. Lee, B. Kim and Y. H. Lee, “On the use of linear programming for dynamic subchannel and bit allocation in multiuser OFDM,” in Proceeding IEEE Global Telecommunications Conference, San Antonio, Texas, USA, vol. 6, pp. 3648–3652, Nov. 2001.
[14].Z. Shen, J.G. Andrews and B.L. Evans, “Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints,” IEEE Transactions on Wireless Communications, vol. 4, no. 6, pp. 2726-2737, Nov. 2005.
[15].P. Viswanath, D. N. C. Tse, and R. Laroia, “Opportunistic beamforming using dumb antennas,” IEEE Transactions on Information Theory, vol. 48, no. 6, pp. 1277–1294, June 2002.
[16].T. M. Cover and J. A. Thomas, Elements of Information Theory, New York: Wiley, 1991.
[17].R. Baldick, Optimization of Engineering Systems Course Notes, Austin, TX: University Texas. [Online]. Available: http://www.ece.utexas.edu/~baldick/
[18].Sergio Barbarossa, Multiantenna Wireless Communication Systems, Artech House, Mar. 2005.
[19].T. S. Rappaport, Wireless Communications: Principles and Practice. Upper Saddle River, New Jersey: Prentice-Hall, 2002.
[20].E. Horowitz, S. Sahni and S. Anderson-Freed, Fundamentals of Data Structures in C, New York: Computer Science Press, 2002.
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