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對於無線網絡運營商來說，聯合優化其經濟和技術方面的決策以獲得商業成功正在變得越來越重要。一個運營商的決策可能包括對技術的選取和部署時間的把握、資源投資的數量，以及針對他所提供的服務的定價。考慮到這些決策彼此之間有關聯，我們需要對這些決策進行聯合優化，特別當運營商面對有限的資源、不成熟的技術和市場競爭時，該優化會變得困難。本論文綜合考慮兩類網絡中的這些因素。注意到在所有絨線技術中蜂窩網絡擁有最廣泛的市場佔有率，我們先研究蜂窩網路中的經濟學。然後我們研究認知無線電中的經濟學，考慮到該技術代表了未來無線技術發展的一個主要趨勢。, 在本論文的第一部分，我們研究一個蜂窩網絡運營商在經濟和技術方面的決策，涉及到網路升級、服務分類和社交應用。首先，我們提出了一套博弈論模型來研究互相競爭的運營商從目前3G 蜂窩技術升級為未來一代(4G) 技術的部署時間。我們的分析指出運營商通常會選擇不同的升級時間以避免激烈的競爭。升級早的運營商在市場佔有方面有優勢，而升級晚的運營商只需承擔少量的升級成本並將面對一個更成熟的4G市場。其次，我們研究一個運營商是否有經濟動機在他已有的蜂窩基站C(macrocell)的基礎上再鋪設家庭基站(femtocell)。家庭基站能解決4G 網絡中室內用戶信號接收差的問題，但是該服務會佔用運營商原本就有限的頻譜資源。最後，我們嘗試去理解一個運營商該如何為異構的智慧手機用戶提供經濟刺激來鼓勵他們協助社交應用(比如，信息收集和分布式計算)的建立。在信息不對稱的情況下，我們設計了有效的激勵機制來根據智慧手機用戶不同的隱私損失、使用能耗和計算效率來提供獎勵。, 在本論文的第二部分，我們在認知其在線電網絡中研究投資的便利性、頻譜感知的不確定性和安全性將如何影響一個次級(沒有頻譜執照的)運營商的決策。首先，我們研究一個可以通過動態頻譜租賞和頻譜感知兩種靈活方式來獲得絨線頻譜的次級運營商。我們聯合研究該運營商的投資選擇和對底層用戶的定價策略來使其利益最優。與動態頻譜租貸相比，頻譜感知不穩定但是能節約投資成本。其次，我們考慮一個包含兩個運營商的競爭市場，並研究他們之間在投資和定價方面的競爭。我們指出該競爭會給底層用戶帶來顯著好處而給運營商們帶來的收益總損失不會超過25% 。最後，一個運營商可能想利用多用戶合作式頻譜感知技術來提高感知的精確性但是該技術容易遭受數據偽造攻擊(data falsification attacks) 。我們通過合適的攻擊檢測和懲罰設計了有效的機制以防範攻擊。, It is becoming increasingly important for wireless network operators to jointly optimize economic and technological decisions for business success. An operator's decisions may involve the choices and timings of technology adoptions, the amount of resources to invest, and the prices to set for his services. These decisions are coupled with each other and need to be jointly optimized, and such optimization will be challenging when the operator faces limited resources, immature technology, and market competition. This thesis focuses on such issues in two types of networks. We first study the economics of cellular networks, which have the largest market occupancy among all wireless technologies. We then look at the economics of cognitive radios networks, which represent one of the main development trends for wireless technologies in the near future., In the first part of this thesis, we study a cellular operator's economic and technological decisions related to network upgrade, service differentiation, and social applications. First, we develop a game theoretic model for studying competitive operators' upgrade timing decisions from the existing 3G cellular technology to the next generation (4G) technology. Our analysis shows that operators often select different upgrade times to avoid severe competition. The operator upgrading earlier has advantage in increasing market share, while the one upgrading later benefit from decreased upgrade cost and a more mature 4G market. Second, we study an operator's economic incentive of deploying femtocell service on top of his existing macrocell service. The femtocell can resolve the issue of poor signal receptions for indoor users in 4G networks, but need to occupy the operator's limited spectrum resources. Finally, we try to understand how an operator can provide economic incentives for the heterogeneous smartphone users to collaborate in social applications (e.g., data acquisition and distributed computing). Under asymmetric information, we design effcient incentive mechanisms that reward smartphone users according to their different sensitivities to privacy loss, energy and computing effciencies., In the second part of this thesis, we study how investment flexibility, sensing uncertainty, and sensing security in cognitive radio networks affect a secondary (unlicensed) operator's decisions. First, we study a secondary operator, who can flexibly acquire wireless spectrum through both dynamic spectrum leasing and spectrum sensing. We jointly study an operator's investment choices and pricing strategy to the end users to maximize his profit. Compared to spectrum leasing, spectrum sensing is unreliable but has a small cost. Second, we consider a competitive market with two operators, and study their competition in both investment and pricing. We show that end users significantly benefit from such market competition, and the operators' total profit loss due to competition is lower bounded by 25% of the maximum. Finally, an operator may want to deploy collaborative spectrum sensing to improve sensing accuracy, but this approach is vulnerable to data falsification attacks. We design effective attack prevention mechanisms through proper attack detection and punishment., Detailed summary in vernacular field only., Duan, Lingjie., Thesis (Ph.D.)--Chinese University of Hong Kong, 2012., Includes bibliographical references (leaves 310-336)., Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web., also in Chinese., p.i, Acknowledgement --- p.vi, Chapter 1 --- Introduction --- p.1, Chapter 1.1 --- Economics of Cellular Networks --- p.3, Chapter 1.2 --- Economic Viability of Cognitive Radio Networks --- p.5, Chapter 1.3 --- Outline and Contributions --- p.9, Chapter I --- Economics of Cellular Networks --- p.13, Chapter 2 --- Economics of 4G Cellular Network Upgrade --- p.14, Chapter 2.1 --- Introduction --- p.14, Chapter 2.1.1 --- Related Work --- p.18, Chapter 2.2 --- System Model --- p.19, Chapter 2.2.1 --- Value of Cellular Networks --- p.19, Chapter 2.2.2 --- User Churn during Upgrade from 3G to 4G Services --- p.23, Chapter 2.2.3 --- Operators' Revenues and Upgrade Costs --- p.25, Chapter 2.3 --- 4G Monopoly Market --- p.26, Chapter 2.4 --- 4G Competition Market: Duopoly Model and Game Formulation --- p.32, Chapter 2.4.1 --- Operators' Long-term Profits --- p.34, Chapter 2.4.2 --- Duopoly Upgrade Game --- p.37, Chapter 2.5 --- 4G Competition Market: No Inter-network switching --- p.39, Chapter 2.6 --- 4G Competition Market: Practical Inter-network Switching Rate --- p.40, Chapter 2.7 --- Summary --- p.50, Chapter 2.8 --- Appendix --- p.50, Chapter 2.8.1 --- Proof Sketch of Theorem 2 --- p.50, Chapter 2.8.2 --- Proof Sketch of Theorem 3 --- p.52, Chapter 3 --- Economics of Femtocell Service Provision --- p.55, Chapter 3.1 --- Introduction --- p.55, Chapter 3.1.1 --- Related Work --- p.60, Chapter 3.2 --- Benchmark: Macrocell Service Only --- p.62, Chapter 3.2.1 --- Users' Bandwidth Demands in Stage II --- p.63, Chapter 3.2.2 --- Operator's Pricing in Stage I --- p.65, Chapter 3.3 --- Provision of Femtocell Service --- p.67, Chapter 3.3.1 --- Users' Service Choices and Bandwidth Demands in Stage II --- p.71, Chapter 3.3.2 --- Operator's Spectrum Allocations and Pricing in Stage I --- p.73, Chapter 3.4 --- Impact of Users' Reservation Payoffs --- p.75, Chapter 3.5 --- Impact of Femtocell Frequency Reuse --- p.82, Chapter 3.6 --- Impact of Femtocell Operational Cost --- p.84, Chapter 3.7 --- Impact of Limited Femtocell Coverage --- p.89, Chapter 3.8 --- Summary --- p.95, Chapter 4 --- Smartphone Collaboration on Social Applications --- p.96, Chapter 4.1 --- Introduction --- p.96, Chapter 4.1.1 --- Related Work --- p.100, Chapter 4.2 --- Collaboration on Data Acquisition Application --- p.102, Chapter 4.2.1 --- System Model of Data Acquisition --- p.102, Chapter 4.2.2 --- Collaboration under Complete Information --- p.105, Chapter 4.2.3 --- Collaboration under Symmetrically Incomplete Information --- p.106, Chapter 4.2.4 --- Collaboration under Asymmetrically Incomplete Information --- p.109, Chapter 4.3 --- Collaboration on Distributed Computing Application --- p.117, Chapter 4.3.1 --- System Model on Distributed Computation --- p.117, Chapter 4.3.2 --- Contractual Interactions between Client and Users --- p.119, Chapter 4.3.3 --- Contract Design under Complete Information --- p.120, Chapter 4.3.4 --- Client's Contract Design under Asymmetrically Incomplete Information --- p.123, Chapter 4.4 --- Summary --- p.133, Chapter 4.5 --- Appendix --- p.134, Chapter 4.5.1 --- Discussion of Model (4.2) in Different Information Scenarios --- p.134, Chapter 4.5.2 --- Proof of Theorem 9 --- p.135, Chapter 4.5.3 --- Proof of No Collaboration and Pure Strategy NE in Theorem 10 --- p.136, Chapter 4.5.4 --- Proof of Existence And Uniqueness of Equilibrium Threshold in Theorem 12 --- p.137, Chapter 4.5.5 --- Proof of Theorem 13 --- p.139, Chapter 4.5.6 --- Proof of Theorem 14 --- p.141, Chapter 4.5.7 --- The proof of Proposition 1 --- p.142, Chapter 4.5.8 --- Approximated Algorithm to Solve Problem (4.23) --- p.146, Chapter II --- Economics of Cognitive Radio Networks --- p.149, Chapter 5 --- Monopoly Spectrum Market Using Cognitive Radios --- p.150, Chapter 5.1 --- Introduction --- p.150, Chapter 5.1.1 --- Related Work --- p.155, Chapter 5.2 --- Network Model --- p.157, Chapter 5.2.1 --- Background on Spectrum Sensing and Leasing --- p.157, Chapter 5.2.2 --- Notations and Assumptions --- p.159, Chapter 5.2.3 --- A Stackelberg Game --- p.163, Chapter 5.3 --- Backward Induction of the Four-stage Game --- p.164, Chapter 5.3.1 --- Spectrum Allocation in Stage IV --- p.165, Chapter 5.3.2 --- Optimal Pricing Strategy in Stage III --- p.167, Chapter 5.3.3 --- Optimal Leasing Strategy in Stage II --- p.171, Chapter 5.3.4 --- Optimal Sensing Strategy in Stage I --- p.173, Chapter 5.4 --- Equilibrium Summary and Numerical Results --- p.179, Chapter 5.4.1 --- Robustness of the Observations --- p.185, Chapter 5.5 --- The Impact of Spectrum Sensing Uncertainty --- p.187, Chapter 5.6 --- Learning the Distribution of Sensing Realization Factor α --- p.191, Chapter 5.6.1 --- Performance Evaluation of Machine Learning --- p.193, Chapter 5.7 --- Summary --- p.195, Chapter 5.8 --- Appendix --- p.196, Chapter 5.8.1 --- Proof of Theorem 18 --- p.196, Chapter 5.8.2 --- Proof of Theorem 19 --- p.197, Chapter 5.8.3 --- Supplementary Proof of Theorem 21 --- p.198, Chapter 6 --- Competitive Spectrum Market Using Cognitive Radios --- p.204, Chapter 6.1 --- Introduction --- p.204, Chapter 6.1.1 --- Related Work --- p.209, Chapter 6.2 --- Network and Game Model --- p.211, Chapter 6.2.1 --- Users' and Operators' Models --- p.213, Chapter 6.3 --- Backward Induction of the Three-Stage Game --- p.215, Chapter 6.3.1 --- Spectrum Allocation in Stage III --- p.217, Chapter 6.3.2 --- Operators' Pricing Competition in Stage II --- p.221, Chapter 6.3.3 --- Operators' Leasing Strategies in Stage I --- p.226, Chapter 6.4 --- Equilibrium Summary --- p.231, Chapter 6.4.1 --- How Network Dynamics Affect Equilibrium Decisions --- p.234, Chapter 6.5 --- Equilibrium Analysis under General SNR Regime --- p.237, Chapter 6.6 --- Impact of Operator Competition --- p.239, Chapter 6.6.1 --- Maximum Profit in the Coordinated Case --- p.239, Chapter 6.6.2 --- Impact of Competition on Operators' Profits --- p.242, Chapter 6.6.3 --- Impact of Competition on Users' Payoffs --- p.244, Chapter 6.7 --- Summary --- p.245, Chapter 6.8 --- Appendix --- p.246, Chapter 6.8.1 --- Proof of Theorem 24 --- p.246, Chapter 6.8.2 --- Proof of Theorem 25 --- p.250, Chapter 7 --- Security Protection in Collaborative Spectrum Sensing --- p.256, Chapter 7.1 --- Introduction --- p.256, Chapter 7.1.1 --- Related Work --- p.260, Chapter 7.2 --- Preliminary --- p.261, Chapter 7.2.1 --- CRN Model and Assumptions --- p.261, Chapter 7.2.2 --- Spectrum Sensing and Opportunistic Access Model --- p.264, Chapter 7.2.3 --- Collision Penalty --- p.265, Chapter 7.2.4 --- Decision Fusion Rule --- p.266, Chapter 7.3 --- Attackers' Behaviors Without Punishment --- p.268, Chapter 7.3.1 --- All SUs sense the channel idle --- p.270, Chapter 7.3.2 --- All honest SUs sense the channel idle, but some attacker(s) senses the channel busy --- p.270, Chapter 7.3.3 --- Some honest SUs sense the channel busy --- p.272, Chapter 7.4 --- Attack-Prevention Mechanism: A Direct Punishment --- p.273, Chapter 7.5 --- Attack-Prevention Mechanism: An Indirect Punishment --- p.277, Chapter 7.6 --- Summary --- p.286, Chapter 7.7 --- Appendix --- p.286, Chapter 7.7.1 --- Relaxation of Assumptions A1 and A3 --- p.286, Chapter 7.7.2 --- Attack Prevention in Case:AT of Section 7.5 --- p.293, Chapter 7.7.3 --- Proof of Lemma 6 --- p.297, Chapter 8 --- Conclusion and Future Work --- p.299, Chapter 8.1 --- Conclusion --- p.299, Chapter 8.2 --- Extensions of 4G Network Upgrade in Chapter 2 --- p.302, Chapter 8.3 --- Extensions of Femtocell Service Provision in Chapter 3 --- p.303, Chapter 8.4 --- Extensions of Smartphone Collaboration on Social Applications in Chapter 4 --- p.304, Chapter 8.5 --- Extensions of Monopoly Spectrum Market in Chapter 5 --- p.305, Chapter 8.6 --- Extensions of Competitive Spectrum Market in Chapter 6 --- p.306, Chapter 8.7 --- Extensions of Security Protection in Collaborative Spectrum Sensing in Chapter 7 --- p.308, Bibliography --- p.310, http://library.cuhk.edu.hk/record=b5549616, Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

社會意見在塑造我們購買決策和購買經歷發揮了至關重要的作用。除了正面（或負面）的意見會鼓勵（或打消）我們購買某個產品，我們的意見更傾向於遵循我們的社交圈。社會意見的這些方面對於做出精確產品推薦、準確預測信息流向、及有效營銷活動發布極為重要。, 在這篇論文中，我們首先研究極性意見對我們的購買決策的影響。同時，我們分析了兩個現實世界中的社會網絡，Flixster 和Epinions 中的消極和積極的意見的信息傳播模式。我們觀察到，否定意見的存在大大降低了表達意見的數量。考慮到這兩種意見的不對稱性，我們提出並擴展了目前最流行的兩個信息傳播模式，獨立分級和線性閾值模型。我們提出的拓展模型提供了一個可處理的影響問題和並能夠提高將來意見的預測精度，超過3%。, 更進一步，我們研究了社會意見對我們表達產品意見的影響。該問題的假設是多次顯示我們表達的意見並不完全獨立於我們的社交圈，而是通過校準，使之跟社會意見相似。為了理解這一現象，我們為用戶的評分提出了一個新型的模型。該模型中，用戶對項目的評分是由社會輿論、用戶的偏好和項目特點的一個函數。該模型可以提高用戶評分的預測準確率達2%。此外，模型中學習到的參數可展示用戶對社會意見的遵循程度。用戶對社會意見的遵循分析表明，超過76%的用戶傾向於在一定程度上遵循他們好友的意見。平均而言，當社會影響存在的時候，用戶評分更趨於正面。我們還發現，社會的遵循者通常不是信息傳播的第一次參與者。, Social opinions play a crucial role in shaping both our purchase decisions and our experience. While on one hand, we are encouraged (discouraged) to adopt a product upon hearing the positive (negative) opinions; on the other hand, our opinions tend to conform to our social circle. Both of these aspects of social opinions are important in order to make precise product recommendations, to accurately predict the information flow pathways and to launch efficient viral marketing campaigns., In this thesis, we first study the impact of polarity of opinions on our purchase decisions. For the same, we analyze the information propagation patterns of the negative and positive opinions on two real world social networks, Flixster and Epinions, and observe that the presence of negative opinions significantly reduces the number of expressed opinions. To account for the asymmetry between the two kinds of opinions, we propose extensions of the two most popular information propagation models, Independent Cascade and Linear Threshold models. The proposed extensions give a tractable influence problem and improve the prediction accuracy of future opinions, by more than 3%., Next, we study the impact of social opinions on our expressed opinions about the products. The hypothesis is that many times our expressed opinions are not completely independent of our social circle and gets calibrated such that they are similar to the social opinions. In order to understand this phenomenon, we propose a novel formulation for the users ratings where every expressed rating is considered as a function of the social opinion along with the user preference and item characteristics. The proposed method helps in improving the prediction accuracy of users’ rating by more than 2% in presence of social influence. Additionally, the learned model parameters reveal the degree of conformity of users. Detailed analysis of user social conformity show that more than 76% of users tend to conform to their friends to some extent. On an average, user ratings become more positive in presence of the social influence. We also nd that the social conformers are usually not the rst one to participate in an information cascade., Detailed summary in vernacular field only., Garg, Priyanka., Thesis (M.Phil.)--Chinese University of Hong Kong, 2012., Includes bibliographical references (leaves 100-110)., s also in Chinese., p.i, Acknowledgement --- p.v, Chapter 1 --- Introduction --- p.1, Chapter 1.1 --- Contributions --- p.3, Chapter 1.2 --- Organization --- p.4, Chapter 2 --- Background & Survey --- p.6, Chapter 2.1 --- Network Structure --- p.6, Chapter 2.1.1 --- Basic Definitions --- p.6, Chapter 2.1.2 --- Structural Properties of Social Networks --- p.8, Chapter 2.1.3 --- Network Generators --- p.12, Chapter 2.2 --- Information Diffusion in Social Networks --- p.16, Chapter 2.2.1 --- Basic Terminologies --- p.19, Chapter 2.2.2 --- Principles governing the Decision-Making process --- p.19, Chapter 2.2.3 --- Information Cascade Models --- p.21, Chapter 2.2.4 --- Influence Estimation --- p.34, Chapter 2.2.5 --- Viral Marketing --- p.39, Chapter 2.2.6 --- Influence vs. Homophily --- p.44, Chapter 2.2.7 --- Results from Large Scale Empirical Studies --- p.45, Chapter 3 --- Impact on Product Purchase Decision --- p.47, Chapter 3.1 --- Introduction --- p.47, Chapter 3.2 --- Related Work --- p.49, Chapter 3.3 --- Problem Definition --- p.50, Chapter 3.4 --- Data and Observations --- p.51, Chapter 3.4.1 --- Data Collection --- p.51, Chapter 3.4.2 --- Observations --- p.52, Chapter 3.5 --- Polarity-Sensitive Information Flow Model --- p.54, Chapter 3.5.1 --- Social Influence Function --- p.55, Chapter 3.5.1.1 --- Polarity-Sensitive IC Model --- p.55, Chapter 3.5.1.2 --- Polarity-Sensitive LT Model (LTPS) --- p.58, Chapter 3.5.2 --- Activation State of Influenced Node --- p.59, Chapter 3.6 --- Influence Estimation --- p.61, Chapter 3.7 --- Experiments on Synthetic Data --- p.63, Chapter 3.7.1 --- IA and WP as Approximation of IC-N --- p.64, Chapter 3.7.2 --- Quality of the Estimated Parameters --- p.65, Chapter 3.7.3 --- Prediction Accuracy --- p.66, Chapter 3.8 --- Experiments on Real Data --- p.69, Chapter 3.8.1 --- Experimental Setup --- p.69, Chapter 3.8.2 --- Observations --- p.70, Chapter 3.9 --- Summary --- p.72, Chapter 4 --- Impact on Posterior Evaluation --- p.73, Chapter 4.1 --- Introduction --- p.73, Chapter 4.2 --- Related Work --- p.75, Chapter 4.3 --- Ratings under social conformity --- p.77, Chapter 4.3.1 --- Problem Definition & Notations --- p.79, Chapter 4.3.2 --- Conformer's Ratings --- p.80, Chapter 4.3.3 --- Parameter Estimation --- p.82, Chapter 4.4 --- Evaluation --- p.84, Chapter 4.4.1 --- Goodreads Dataset --- p.85, Chapter 4.4.2 --- Prediction Accuracy --- p.87, Chapter 4.4.3 --- Influencers Quality --- p.89, Chapter 4.5 --- Social Conformity Analysis --- p.91, Chapter 4.6 --- Summary --- p.96, Chapter 5 --- Summary & Future Work --- p.97, Chapter 5.1 --- Summary --- p.97, Chapter 5.2 --- Future Work --- p.98, Bibliography --- p.100, http://library.cuhk.edu.hk/record=b5549126, Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

After that, the cooperative transmission scheme is extended for the scenario of more than two source-destination pairs. One objective is to investigate the relationship between the diversity order and the number of source-destination pairs. This is done by considering the sum power minimization problem. A pricing game is derived to provide a distributed implementation. At Nash Equilibrium of the game, the total transmission power is minimized. Simulation results show the rapid convergence of the game and its adaptation to channel fluctuations. It also shows that the cooperative transmission scheme achieves full diversity order., Apart from replacing a superposition code based cooperative transmission scheme by a TDM based scheme, the implementation can be simplified by introducing a partner selection scheme to the nodes. In that network, the cooperative transmission code still uses superposition code as the building block. Instead of relaying the messages from all other nodes, in this new scheme, the source nodes only relay the messages for their assigned partners. A natural question is: How can we assign the partners to the source nodes such that the total transmission power is minimized. The problem is solved in two phases. Firstly, we solve the sum power minimization problem for each pair of nodes. In some cases, this problem has closed-form solutions while for the other cases, a simple iterative algorithm can solve this problem., Firstly, cooperative orthogonal-division channel is defined and two cooperative transmission schemes based on dirty-paper coding and superposition code are proposed and compared through simulations. Simulation Results show the significant improvement over the pure direct transmission schemes. Although one cooperative transmission scheme achieves a slightly larger rate region, the other scheme has a much simpler implementation so the remaining parts of the thesis focus on this scheme. The outage performance of this scheme is also compared with a simplified Han-Kobayashi scheme through simulations. Simulation results illustrate the significant improvement in the diversity gain of this scheme over the Han-Kobayashi scheme., However, it is noted that the complexity of implementing superposition code, which is a building block of the cooperative transmission code, is very high when there are many users in the network. Hence, another time-division multiplex (TDM) based cooperative transmission scheme is proposed. Similar to the superposition code based scheme, there is a pricing game which can provide a distributed sum power minimization. Simulation results also show that the game has high convergence rate and it can adapt to changes of channel conditions efficiently. In addition, this cooperative transmission scheme also achieves full diversity order., In this thesis, different codes and resource allocation algorithms for cooperative transmissions are proposed. Briefly speaking, in cooperative transmission, a number of wireless nodes form a coalition in which they exchange and cooperatively transmit messages. As a result, the order of diversity can be increased without installing additional antennas., Next, a weighted sum rate maximization algorithm is proposed. There are two purposes of this algorithm. Firstly, this algorithm is adopted to find the Pareto-optimal points of the boundary of the achievable rate region through simulations. Secondly, this algorithm can be extended to solve the max-min fairness problem and the joint utility maximization algorithm by the proposed framework., This thesis is ended with some future research directions., With this information, we can assign the partners by Gabow's algorithm, which solves the maximum weighted matching problem that is mapped from the original partner selection problem. Nonetheless, it is noted that when the number of users is very large, it involves a large amount of the communication and computational cost to solve the sum power minimization problem for each pair of nodes as well as the partner selection problem. Therefore, the Grouping Algorithm is proposed to reduce the aforementioned implementation cost. Simulation results show that the optimal algorithm and the Grouping Algorithm can achieve full diversity order. Moreover, although the Grouping Algorithm is sub-optimal in general, it costs only 1dB of the sum power more than the optimal algorithm., Ng, Cho Yiu., Adviser: Tal M. Lok., Source: Dissertation Abstracts International, Volume: 73-03, Section: B, page: ., Thesis (Ph.D.)--Chinese University of Hong Kong, 2010., Includes bibliographical references (leaves 152-162)., Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web., Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web., also in Chinese., isbn: 9781267008909, Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)