基于博弈论的认知网络频谱共享研究
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摘要
在无线网络中频谱是一种稀缺的资源。而认知无线电技术能提高频谱利用率。传统情况下,无线频谱静态分配给授权的无线用户,然而一些频谱在一些时间段和某些位置大量地未使用,我们称这些频谱为频谱洞或频谱机会。认知无线电利用这些频谱机会改善了频谱利用率和网络性能。基于软件定义无线电的发展,认知收发设备具有感知所处的无线电环境,并根据环境变化自适应调整系统参数等捷变特性,这些特性使得认知用户能够共享授权用户的频谱资源,从而提高频谱利用率,同时增加频谱所有者的收益。
然而认知领域大多数研究工作强调频谱共享的技术方面,如频谱检测、适于动态频谱接入的协议、动态无线资源控制等。本文研究频谱共享的经济方面,使用频谱贸易表示认知环境中买卖无线资源的过程。
本文所作的几点工作如下：
一、本文的第二章简要介绍了认知网络架构,讨论了频谱管理功能：频谱检测、频谱决策、频谱共享、频谱移动。描述了认知无线电的不同网络架构、协议行为及不同的频谱共享模型。阐述了频谱贸易的动机和必要性,分析了频谱贸易的不同结构、相关的研究问题、可能的解决方案。从网络用户行为分析、有效的动态分布式设计两方面综述了基于博弈理论的动态频谱共享。
二、本文的第三章研究了认知网络环境下的上行功率控制定价问题,即认知用户理性地调整上行传输功率,目标最大化自身的净效用,而主服务提供商根据认知用户发送的功率大小向认知用户收取一定的费用,增加收益。用非合作博弈描述了认知用户的竞争行为,论证了纳什均衡的存在性和唯一性。基于唯一均衡,描述了主服务提供商的定价问题为非凸优化问题,提出了一个次优的定价方案,该方案具有一定的公平性。
三、本文的第四章将多个主服务提供商间的价格竞争建模为寡头博弈,纳什均衡和斯塔克伯格均衡分别作为同步行动博弈和领导者-追随者博弈的解。为获得最高总收益,同时考虑了合作定价,给出了两种公平的收益分配方案。
四、本文的第五章呈现了一个层次化频谱贸易模型,分析了电视广播机构、运营商、用户驻地设备间的交互。将多个电视广播机构和多个运营商间的频谱贸易建模为双向拍卖,给出了简单易行的算法。使用演化博弈理论描述认知用户在选择运营商时的动态行为,用中心化算法实行网络选择的演化过程。用非合作博弈描述运营商间的接入价格竞争。为追求最大化利润,运营商应合理地联合决定所需的电视信道数和业务接入价格。
Frequency spectrum is the scarcest radio resource in wireless communication networks. The concept of cognitive radio was introduced to improve the frequency spectrum utilization in wireless networks. Traditionally, radio spectrum is statically allocated to licensed wireless users. However, it is observed that some frequency bands in the radio spectrum are largely unused in any time and location. These are referred to as spectrum holes (or spectrum opportunities). Cognitive radio takes advantage of these spectrum opportunities to improve spectrum utilization and network performance. Developed based on software-defined radio, a cognitive radio transmitter can adaptively and intelligently change the transmission parameters in a dynamic environment. With cognitive radio, frequency spectrum can be efficiently shared among multiple users to improve spectrum usage. From an economic viewpoint, it can generate more revenue for the spectrum owner and also enhance the satisfaction of cognitive radio users.
While most of the work in the area of cognitive radio emphasized the technical aspect of spectrum sharing (e.g., spectrum sensing, protocol for dynamic spectrum access, dynamic radio resource control), in this article we focus on the economic aspect of spectrum sharing. We use the term spectrum trading to refer to the process of selling and buying radio resource (e.g., spectrum) in a cognitive radio environment.
The work in this dissertation is concluded as following points.
Firstly, a brief overview of the CR network architecture is provided. Then four main functions of spectrum management are discussed:spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. We describe the different network architectures and protocol behaviors for cognitive radio as well as the different spectrum sharing models. The motivation and necessity for spectrum trading are stated. Also, the scope of spectrum trading in the context of dynamic spectrum access is discussed. Different structures of spectrum trading, the related research issues, and the possible solution approaches are presented. This article provides a game theoretical overview of dynamic spectrum sharing from two aspects: analysis of network users'behaviors, efficient dynamic distributed design.
Secondary, in chaper 3, we study the pricing issue in a competitive cognitive radio network in which the secondary users strategically adjust their uplink transmission power levels to maximize their own utilities, and the primary service provider (e.g., base station) charges the secondary users on their transmitted power levels to enhance its own revenue. We model the competitive behavior of the secondary users as a non-cooperative game and address the existence and uniqueness of Nash equilibrium. Based on the unique equilibrium, we formulate the pricing problem for the primary service provider as a non-convex optimization problem. We propose a sub-optimal pricing scheme in terms of revenue maximization of the primary service provider, and we claim that this scheme is fair in terms of power allocation among secondary users.
Thirdly, in chaper 4, we propose two oligopolistic models for price competition among primary service providers. A non-cooperative game is formulated to obtain the price. The Nash and Stackelberg equilibrium are considered as the solutions of the simultaneous-move and leader-follower price competitions, respectively. Furthermore, we have considered cooperative pricing model where all of the service providers can cooperate to achieve the highest total revenue. Different fairness criteria are chosen for apportioning the coalition worth among its members.
Fourthly, in chaper 5, A hierarchical spectrum trading model is presented to analyze the interaction among TV broadcasters, WRAN service providers, and WRAN users. In this model a double auction is established among multiple TV broadcasters and WRAN service providers who sell and buy the radio spectrum, respectively. In particular, the theory of evolutionary games is used to investigate the dynamics of WRAN user behavior and solution in network selection. A centralized algorithm is proposed to implement the proposed evolutionary game model for network selection. Then, multiple WRAN service providers compete with each other by adjusting the service price charged to WRAN users. To model the competition, a non-cooperative game is formulated. In order to maximize their own profits, every WRAN service provider should seek the optimal spectrum bidding and service pricing strategy.
Finally, a conclusion is drawn for the dissertation, and valuable research directions in the future are presented.
然而认知领域大多数研究工作强调频谱共享的技术方面,如频谱检测、适于动态频谱接入的协议、动态无线资源控制等。本文研究频谱共享的经济方面,使用频谱贸易表示认知环境中买卖无线资源的过程。
本文所作的几点工作如下：
一、本文的第二章简要介绍了认知网络架构,讨论了频谱管理功能：频谱检测、频谱决策、频谱共享、频谱移动。描述了认知无线电的不同网络架构、协议行为及不同的频谱共享模型。阐述了频谱贸易的动机和必要性,分析了频谱贸易的不同结构、相关的研究问题、可能的解决方案。从网络用户行为分析、有效的动态分布式设计两方面综述了基于博弈理论的动态频谱共享。
二、本文的第三章研究了认知网络环境下的上行功率控制定价问题,即认知用户理性地调整上行传输功率,目标最大化自身的净效用,而主服务提供商根据认知用户发送的功率大小向认知用户收取一定的费用,增加收益。用非合作博弈描述了认知用户的竞争行为,论证了纳什均衡的存在性和唯一性。基于唯一均衡,描述了主服务提供商的定价问题为非凸优化问题,提出了一个次优的定价方案,该方案具有一定的公平性。
三、本文的第四章将多个主服务提供商间的价格竞争建模为寡头博弈,纳什均衡和斯塔克伯格均衡分别作为同步行动博弈和领导者-追随者博弈的解。为获得最高总收益,同时考虑了合作定价,给出了两种公平的收益分配方案。
四、本文的第五章呈现了一个层次化频谱贸易模型,分析了电视广播机构、运营商、用户驻地设备间的交互。将多个电视广播机构和多个运营商间的频谱贸易建模为双向拍卖,给出了简单易行的算法。使用演化博弈理论描述认知用户在选择运营商时的动态行为,用中心化算法实行网络选择的演化过程。用非合作博弈描述运营商间的接入价格竞争。为追求最大化利润,运营商应合理地联合决定所需的电视信道数和业务接入价格。
Frequency spectrum is the scarcest radio resource in wireless communication networks. The concept of cognitive radio was introduced to improve the frequency spectrum utilization in wireless networks. Traditionally, radio spectrum is statically allocated to licensed wireless users. However, it is observed that some frequency bands in the radio spectrum are largely unused in any time and location. These are referred to as spectrum holes (or spectrum opportunities). Cognitive radio takes advantage of these spectrum opportunities to improve spectrum utilization and network performance. Developed based on software-defined radio, a cognitive radio transmitter can adaptively and intelligently change the transmission parameters in a dynamic environment. With cognitive radio, frequency spectrum can be efficiently shared among multiple users to improve spectrum usage. From an economic viewpoint, it can generate more revenue for the spectrum owner and also enhance the satisfaction of cognitive radio users.
While most of the work in the area of cognitive radio emphasized the technical aspect of spectrum sharing (e.g., spectrum sensing, protocol for dynamic spectrum access, dynamic radio resource control), in this article we focus on the economic aspect of spectrum sharing. We use the term spectrum trading to refer to the process of selling and buying radio resource (e.g., spectrum) in a cognitive radio environment.
The work in this dissertation is concluded as following points.
Firstly, a brief overview of the CR network architecture is provided. Then four main functions of spectrum management are discussed:spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. We describe the different network architectures and protocol behaviors for cognitive radio as well as the different spectrum sharing models. The motivation and necessity for spectrum trading are stated. Also, the scope of spectrum trading in the context of dynamic spectrum access is discussed. Different structures of spectrum trading, the related research issues, and the possible solution approaches are presented. This article provides a game theoretical overview of dynamic spectrum sharing from two aspects: analysis of network users'behaviors, efficient dynamic distributed design.
Secondary, in chaper 3, we study the pricing issue in a competitive cognitive radio network in which the secondary users strategically adjust their uplink transmission power levels to maximize their own utilities, and the primary service provider (e.g., base station) charges the secondary users on their transmitted power levels to enhance its own revenue. We model the competitive behavior of the secondary users as a non-cooperative game and address the existence and uniqueness of Nash equilibrium. Based on the unique equilibrium, we formulate the pricing problem for the primary service provider as a non-convex optimization problem. We propose a sub-optimal pricing scheme in terms of revenue maximization of the primary service provider, and we claim that this scheme is fair in terms of power allocation among secondary users.
Thirdly, in chaper 4, we propose two oligopolistic models for price competition among primary service providers. A non-cooperative game is formulated to obtain the price. The Nash and Stackelberg equilibrium are considered as the solutions of the simultaneous-move and leader-follower price competitions, respectively. Furthermore, we have considered cooperative pricing model where all of the service providers can cooperate to achieve the highest total revenue. Different fairness criteria are chosen for apportioning the coalition worth among its members.
Fourthly, in chaper 5, A hierarchical spectrum trading model is presented to analyze the interaction among TV broadcasters, WRAN service providers, and WRAN users. In this model a double auction is established among multiple TV broadcasters and WRAN service providers who sell and buy the radio spectrum, respectively. In particular, the theory of evolutionary games is used to investigate the dynamics of WRAN user behavior and solution in network selection. A centralized algorithm is proposed to implement the proposed evolutionary game model for network selection. Then, multiple WRAN service providers compete with each other by adjusting the service price charged to WRAN users. To model the competition, a non-cooperative game is formulated. In order to maximize their own profits, every WRAN service provider should seek the optimal spectrum bidding and service pricing strategy.
Finally, a conclusion is drawn for the dissertation, and valuable research directions in the future are presented.
引文
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[9]Dusit Niyato, Ekram Hossain, "Competitive spectrum sharing in cognitive radio networks:a dynamic game approach," IEEE Transactions on Wireless Communications, vol.7, no.1, pp. 2651-2660,2008.
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[1]C. Cordeiro et al, "IEEE 802.22:The First Worldwide Wireless Standard Based on Cognitive Radios," Proc. IEEE DySPAN, pp.328-37, Nov.2005
[2]Stevenson, C; Caldwell, W.; "IEEE 802.22:The first cognitive radio wireless regional area network standard," IEEE Communications Magazine, Volume 47, Issue 1, pp.130-138, January 2009
[3]S. Sengupta, M. Chatterjee, and S. Ganguly, "An Economic Framework for Spectrum Allocation and Service Pricing with Competitive Wireless Service Providers," IEEE DySPAN 2007, pp.89-98
[4]Hyoil Kim, KANG G. Shin, "Understanding Wi-Fi 2.0:from the economical perspective of wireless service providers,"IEEE Wireless Communications,pp.41-46,August 2010
[5]D.Niyato, E.Hossain, Zhu Han, "Dynamic spectrum access in IEEE 802.22-based cognitive wireless networks:a game theoretic model for competitive spectrum bidding and pricing," IEEE Wireless Communications,16(2):16-23,2009
[6]Niyato, D.; Hossain, E.; Zhu Han; "Dynamics of Multiple-Seller and Multiple-Buyer Spectrum Trading in Cognitive Radio Networks:A Game-Theoretic Modeling Approach," IEEE Transactions on Mobile Computing, Volume 8, Issue 8, pp.1009-1022, Aug.2009
[7]Beibei Wang, K. J. Ray Liu,T. Charles Clancy, "Evolutionary Cooperative Spectrum Sensing Game:How to Collaborate?" IEEE TRANSACTIONS ON COMMUNICATIONS, VOL.58, NO.3, MARCH 2010
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