Pricing communication networks: optimality and Incentives.

• Other Authors: Li, Shuqin
• Format: Others
• Language: English; Chinese
• Published: 2012
• Subjects: Wireless communication systems; Mobile communication systems
• Online Access: http://library.cuhk.edu.hk/record=b5549583; http://repository.lib.cuhk.edu.hk/en/item/cuhk-328060

網絡定價是一個基于對網絡技術和微觀經濟學深刻理解而產生和發展的交叉學科。其目標在於通過合理分配稀缺的網絡資源以滿足不同用戶的服務質量，同時又兼顧考慮對網絡中各個不同實體的相應激勵，以而實現令人滿意的網絡性能。適宜的定價設計在通訊網絡的運營和管理中都是必不可缺的。在本論文中，我們將網絡定價分為四類:面向優化的靜態定價、面向優化的動態定價、利潤驅動的靜態定價，和利潤驅動的動態定價。第一類定價問題已經在文獻中深入討論，本論文將集中討論後三類定價問題。對于每一類定價問題，我們將通過一個網絡定價設計實例來闡明定價設計中的關鍵挑戰與深刻見解。 === 首先，我們研究了利潤驅動的靜態定價。我們考慮了一個壟斷型的網絡運營商的利潤最大化問題，討論其如何設計激勵相容的價格，主人而使得有限的網絡資源在不同類型用戶間合理地分配。我們通過完全信息和非完全信息下的雙層斯塔伯格博博弈模型來建模刻畫運營商和用戶之間的相互作用。在完全信息下，我們研究了三種定價策略:完全價格分化、部分價格分化，和無價格分化。我們分析了這些不同定價策略在系統性能和複雜度之間的權衡關係。在不完全信息下，我們展示了設計價格分化策略的可能性，並且給出這種激勵相容的定份策略能使運營商獲得完全信息下價格分化定價策略所獲得之相同收益的充分必要條件。 === 接著，我們研究了利潤驅動的動態定價。我們考慮了一個認知網絡虛擬移動網絡運營商的資源分配勻利潤最大化的一般問題。認知網絡的動態性包括動態的用戶需求、不穩定的檢測頻譜資源、動態的頻譜租用價格，以及時變的無線信道條件。另外，為使網絡模型更接近于現實，我們還考慮了多用戶差異性和有缺陷的頻譜檢測技術。我們設計和展了一套低複雜度的在線控制策略，能夠在不知動態網絡參數的統計特性的情況下，確定定價和資源分配。我們證明這套動態定價的算法在適當權衡網絡延時的條件下，可以無限趨近最大利潤。 === 最後，我們研究了面向優化的動態定價。我們考慮一個節點容量受限的拓撲時變的多播網絡。通過運用網絡編碼，我們設計了一套動態定價策略可以分佈式地實現無限趨近最優的網絡性能。另外，我們證明這套算法是激勵相容的. 即無論節點在網絡中充當任何角色，該算法都可以保證該節點獲得非負的收益。這個結果表明，該算法可以給網絡節點提供有效的激勵，使之加入網絡、停留在網絡中，並且即使在沒有自身感興趣內容時，也願意充當其他節點的中繼，這一結果在多用戶的節點容量受限網絡(如P2P 網絡)的構建中有著重要的現實意義。 === 以上本論文推導之結果都展示了網絡定價在通訊網絡中的重要意義。尤其顯示了網絡定價是實現最優網絡性能，同時對各網絡實體提供激勵的有效工具。本論文不僅幫助我們更好地理解網絡定價問題，同時也給出網絡定價設計中的深刻見解。 === Network pricing is a cross-disciplinary research area, which requires deep understanding of both networking technology and microeconomics. The goal of network pricing is to achieve satisfied network performances by allocating the scarce resource to satisfy different users’ qualities of services while keeping in mind the incentives of different network entities. Proper design of pricing schemes is indispensable to the operation and management of communication networks. In this thesis we divide network pricing into four categories: static optimization-oriented pricing, dynamic optimization-oriented pricing, static profit-driven pricing, and dynamic profit-driven pricing. The first one is well studied in the literature, and our focus will be on the latter three categories. For each category, we illustrate the key design challenges and insights through a concrete networking example. === First, we investigate the issue of static profit-driven pricing. We consider a revenue maximization problem for a monopolist service provider, and discuss how to set incentive-compatible prices to induce proper allocation of limited resources among different types of users. We capture the interaction between the service provider and users through a two-stage Stackelberg game with both complete and incomplete information. With complete information, we study three pricing schemes: complete price differentiation, partial price differentiation, and no price differentiation. We characterize the trade-offs between the performance and complexity of different schemes. With incomplete information, we show that it is still possible to realize price differentiation, and provide the sufficient and necessary condition under which an incentive compatible price differentiation scheme can achieve the same revenue as the best scheme with complete information. === Then we investigate the issue of dynamic profit-driven pricing. We consider a general resource allocation and profit maximization problem for a cognitive virtual mobile network operator. Dynamics of the cognitive radio network include dynamic user demands, unstable sensing spectrum resources, dynamic spectrum prices, and time-varying channel conditions. In addition, we also consider multiuser diversity and imperfect sensing technique so that the network model is more realistic. We develop a low-complexity on-line control policy that determines pricing and resource scheduling without knowing the statistics of dynamic network parameters. We show that the proposed algorithm with dynamic pricing can achieve arbitrarily close to the optimal profit with a proper trade-off with the queuing delay. === We later investigate the issue of dynamic optimization-oriented pricing. We consider a node-capacitated multicast network with time-varying topology. By utilizing network coding, we design a dynamic pricing scheme that can achieve arbitrarily close to maximum network utility in a distributed fashion, while maintaining network stability. Moreover, we show that this algorithm is incentivecompatible, i.e., no matter what role a node plays in the network, the algorithm guarantees that the node has a non-negative profit. This result has practical importance for constructions for node-capacitated networks with multiple individual users (e.g., P2P networks), since it provides the proper incentives for individual nodes to join, stay, and contribute as relays in the network even if they have no interested contents. === The results developed in this thesis highlight the importance of pricing in communication networks. Specifically, our results show that pricing can be used as an effective tool to achieve optimal network performances while providing proper incentives for all network entities. This not only helps us better understand network pricing, but also gives us insights on the design of network pricing schemes. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Li, Shuqin. === Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. === Includes bibliographical references (leaves 155-168). === Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. === Abstract also in Chinese. === Abstract --- p.i === Acknowledgement --- p.vi === Contents --- p.viii === List of Figures --- p.xii === List of Tables --- p.xv === Chapter 1 --- Introduction --- p.1 === Chapter 1.1 --- Pricing Schemes in Communication Networks --- p.3 === Chapter 1.2 --- Two Main Algorithm Design Techniques --- p.5 === Chapter 1.2.1 --- Network Utility Maximization --- p.5 === Chapter 1.2.2 --- Lyapunov Stochastic Optimization --- p.7 === Chapter 1.3 --- Thesis Outline --- p.10 === Chapter 2 --- Price Differentiation for Communication Networks --- p.13 === Chapter 2.1 --- Usage-based Pricing Schemes --- p.14 === Chapter 2.2 --- System Model --- p.17 === Chapter 2.3 --- Complete Price Differentiation under complete information --- p.20 === Chapter 2.3.1 --- User’s Surplus Maximization Problem in Stage 2 --- p.20 === Chapter 2.3.2 --- Service Provider’s Pricing and Admission Control Problem in Stage 1 --- p.20 === Chapter 2.3.3 --- Properties --- p.25 === Chapter 2.4 --- Single Pricing Scheme --- p.26 === Chapter 2.4.1 --- Problem Formulation and Solution --- p.27 === Chapter 2.4.2 --- Properties --- p.28 === Chapter 2.5 --- Partial Price Differentiation under Complete Information --- p.32 === Chapter 2.5.1 --- Three-level Decomposition --- p.33 === Chapter 2.5.2 --- Solving Level-2 and Level-3 --- p.36 === Chapter 2.5.3 --- Solving Level-1 --- p.39 === Chapter 2.6 --- Price Differentiation under Incomplete Information --- p.43 === Chapter 2.6.1 --- Extensions to Partial Price Differentiation under Incomplete Information --- p.48 === Chapter 2.7 --- Connections with the Classical Price Differentiation Taxonomy --- p.49 === Chapter 2.8 --- Numerical Results --- p.50 === Chapter 2.8.1 --- When is price differentiation most beneficial? --- p.50 === Chapter 2.8.2 --- What is the best tradeoff of Partial Price Differentiation? --- p.56 === Chapter 2.9 --- Summary --- p.58 === Chapter 2.10 --- Appendix of Chapter 2 --- p.59 === Chapter 2.10.1 --- Complete Price Differentiation under complete information with General Utility Functions --- p.59 === Chapter 2.10.2 --- Proof of Proposition 2.1 --- p.64 === Chapter 2.10.3 --- Proof of Lemma 2.2 --- p.65 === Chapter 2.10.4 --- Proof of Theorem 2.4 --- p.66 === Chapter 2.10.5 --- Proof of Theorem 2.6 --- p.72 === Chapter 3 --- Profit Maximization of Cognitive Mobile Virtual Network Operator in A DynamicWireless Network --- p.73 === Chapter 3.1 --- Dynamic Spectrum Access --- p.74 === Chapter 3.2 --- Related Work --- p.77 === Chapter 3.3 --- System Model --- p.79 === Chapter 3.3.1 --- Imperfect Spectrum Sensing --- p.81 === Chapter 3.3.2 --- Collision Constraint --- p.82 === Chapter 3.3.3 --- Spectrum Leasing with Dynamic Market Price --- p.82 === Chapter 3.3.4 --- Power Allocation --- p.83 === Chapter 3.3.5 --- Demand Model --- p.84 === Chapter 3.3.6 --- Queuing dynamics --- p.86 === Chapter 3.4 --- Problem Formulation --- p.87 === Chapter 3.5 --- Profit Maximization Control (PMC) Policy --- p.89 === Chapter 3.5.1 --- Lyapunov stochastic optimization --- p.89 === Chapter 3.5.2 --- Profit Maximization Control (PMC) policy --- p.93 === Chapter 3.5.3 --- Algorithms for Cost Minimization Problem --- p.96 === Chapter 3.5.4 --- Performance of the PMC Policy --- p.101 === Chapter 3.5.5 --- Extension: More General Model of Primary Users’ Activities --- p.102 === Chapter 3.6 --- Heterogeneous Users --- p.104 === Chapter 3.6.1 --- Multi-queue Profit Maximization Control (M-PMC) Policy --- p.106 === Chapter 3.6.2 --- Performance of the M-PMC Policy --- p.111 === Chapter 3.7 --- Simulation --- p.112 === Chapter 3.8 --- Summary --- p.116 === Chapter 3.9 --- Appendix of Chapter 3 --- p.118 === Chapter 3.9.1 --- (Waterfilling) Power Allocation Algorithm --- p.118 === Chapter 3.9.2 --- Threshold Searching Algorithm --- p.118 === Chapter 3.9.3 --- Proof for Theorem 3.2 (a) --- p.119 === Chapter 3.9.4 --- Proof for Theorem 3.2 (b) --- p.121 === Chapter 3.9.5 --- Impact of Queueing on Revenue Maximization --- p.123 === Chapter 4 --- Distributed Resource Allocation for Node-Capacitated Networks with Network Coding --- p.126 === Chapter 4.1 --- Node-Capacitated Networks --- p.126 === Chapter 4.2 --- Network Model --- p.132 === Chapter 4.2.1 --- Time-varying network topology and node upload capacities --- p.132 === Chapter 4.2.2 --- Multicast with intra-session network coding --- p.133 === Chapter 4.3 --- Stochastic Network Utility Maximization Problem --- p.135 === Chapter 4.4 --- Low Complexity Distributed Algorithm --- p.138 === Chapter 4.5 --- Performance Analysis --- p.141 === Chapter 4.5.1 --- Network Stability --- p.141 === Chapter 4.5.2 --- Network Utility Maximization --- p.143 === Chapter 4.5.3 --- The Incentives Issue --- p.146 === Chapter 4.6 --- Summary --- p.150 === Chapter 5 --- Conclusion --- p.151 === Chapter 5.1 --- Extensions on Static Profit-driven Pricing --- p.152 === Chapter 5.2 --- Extensions on Dynamic Profit-driven Pricing --- p.153 === Chapter 5.3 --- Extensions on Dynamic Optimization-oriented Pricing --- p.153 === Bibliography --- p.155