认知无线电中的资源分配研究
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摘要
频谱是基本的无线通信资源。随着无线通信技术的发展,目前的无线用户服务重心已向视频、音频、高清晰图片和流媒体等需要较宽频谱和较高下载速率的服务转移,而现有的固定式频谱管理已成为高性能数据服务和新无线技术发展的阻碍。
在很多国家,大量的可用频谱资源已被分配。尽管如此,美国联邦通信委员会(FCC)频谱策略任务工作报告指出,在很多个地区的大部分时间段,多数的频谱都处于空闲状态,这说明频谱的缺乏主要是由频谱分配机制引起的而不是真正意义上的频谱缺乏。在现有的固定式频谱分配方式中,频谱授权只能在固定的地方、用特定的技术、提供特定的无线服务,并且不允许转让使用权或者更换技术,从而导致了很低的频谱资源利用率。
对于只关心服务而不关心技术的用户而言,网络运行商在保证他们的利益最大化的前提之下,应该为用户所需的服务提供最适合的技术。然而,目前的情况是用户只能得到运行商提供的某些特定服务,并用这些服务会受到现有技术的限制以及频谱管理政策的约束。为了更好地适应用户的需求的变化,网络以及终端应该在服务类型、技术和频谱应该等方面具有更高的灵活性。
在2000年,Joseph MitolaⅢ提出了认知无线电(Cognitive Radio,CR)的概念。认知无线电是可以感知外界通信环境的智能通信系统。通过认知无线电技术可以动态地增加网络和个人用户的可用频谱总数,从而为频谱分配提供了一个可能的解决方案,同时可为网络及其终端提供更高的灵活度。因而对无线网络资源分配的研究具有重大意义。
本论文对基于认知无线电的频谱资源分配开展了研究。为了达到最大频谱使用效益,频谱资源需要合理的动态分配。研究的内容包括:(1)首先是如何分配频谱才能达到最大使用效益的问题。主要内容是研究频谱分配方式的统一理论框架。(2)从短期频谱需求来看,要提出并分析从现有的固定频谱分配的环境中免费地获取频谱资源即机会性频谱接入的方法(Opportunistic SpectrumAccess)。(3)在基于机会性频谱接入方式下如何进行功率和子频段分配。主要研究基于统计的功率和子频段分配以及软频谱获取问题。(4)从长期频谱需求来看,要提出并分析基于收费的动态频谱转让(Dynamic Spectrum Leasing)机制。先提出能基于费用频谱交换的框架,然后研究频谱定价方法以及方法的最优性分析。(5)在基于动态频谱转让方式下进行功率和子频段分配。主要研究基于价格的功率和子频段分配问题。
本论文的主要创新点包括:
创新点一:针对不同的频谱分配模式提出了统一的理论研究框架,在该框架之下,动态的频谱分配方式允许频谱使用权的转让,因而能带来最大的使用效用,是最优的分配方式,固定的分配方式为其次,而自由的频谱使用为最次。更详细的介绍在下两段。
虽然多方提倡基于市场的动态频谱分配机制,但是,在现有的文献并没有从理论的角度给出统一的理论研究框架。所谓统一的理论研究框架是指能用于研究固定分配模式(Command and Control)、动态频谱转让(Dynamic SpectrumLeasing)以及自由共享(Spectrum Commons)的频谱分配机制。本论文针对这三种频谱分配机制提出了统一的理论研究框架,从而分析了它们对频谱使用所带来的效益。该框架基于一个明显的事实,即由于频谱管理一般是由政府机关来控制,因此在某种意义上可以认为是国家垄断的一项业务,而把对某频段使用授权视为垄断的产品。因此,本论文的研究思路是采取耐用品垄断(Durable GoodsMonopoly)理论来建立该框架。基于此就可以得到一个比较以上三种频谱分配方式的框架。该框架指出动态的(允许频谱使用权的转让)频谱分配方式为最优(能带来最大的使用效益),固定的分配方式为其次,而自由的频谱使用为最次。本文提出对基于价格的频谱分配而言,达到帕累托最优(Pareto Optimality)的条件是频谱管理者可以知道每个用户所愿意付出的代价,并因此选出能出最高的频谱价化转让给它使用。帕累托最优是指资源分配的一种状态,在不使任何人境况变坏的情况下,无法再使某些人的处境变好。
上面的理论框架跟部分国家的未来频谱管理计划吻合。英国通信办公室(OFCOM)的未来频谱分配计划显示:现在固定的频谱使用机制占96%,而2010年以后只占22%;动态的频谱转让机制目前占0%,而将来占71%;自由的频谱使用机制目前占4%,而将来占7%。美国的FCC以及其他国家如新西兰、澳大利亚、中国台北等也都提出了类似的趋势,即频谱的动态转让机制。从长期发展来看,我们应该抓紧研究可以灵活地获得频谱资源的通信技术,否则到时候技术落后于政策将导致失去'先下手为强'的优势。从实际情况来看,大部分国家的政策还不支持灵活的频谱分配,所以作为一个短期的方案还是需要考虑如何从现有的固定的频谱分配机制中获取免费的频谱资源。
本论文考虑了这两种情况,即认知无线电技术中,如何在固定(免费)和动态(收费)频谱分配机制下进行资源分配。所考虑的资源为政策中的频谱分配和系统中的功率和子频段分配。基于以上的分析比较结果,本文进而围绕着频谱免费和收费两种情况进行频谱、功率和子频段资源分配的研究:针对免费频谱获取,研究基于机会频谱接入方式以及基于统计的软频谱获取,功率和子频段分配;针对收费的频谱分配研究是以动态频谱转让方式和基于价格的功率和子频段分配来实现的。
创新点二:针对固定的频谱分配方式中的免费频谱获取,提出了用于机会频谱接入的持续可调(Variable Persistence)的感知方法,以填满频谱空洞并提高频谱的利用率。具体提出了三个频谱感知方案,并用对所提出的方案进行了理论分析和数值仿真实验。
由于固定分配机制频谱资源利用的不灵活,导致了很低的频谱利用效率。这部分在解决如何最有效地再利用频谱资源?这一问题。针对固定的频谱分配方式,提出了用于机会频谱接入的持续可调的感知方法,以填满频谱'空洞'并提高频谱的利用率。持续度可调是指感知某一个子频段的时隙数可以调整,而频谱空洞是指那些已分配给授权用户使用,但是在特定的时间和地点授权用户却没有使用的频段。具体提出了三个频谱感知方案,并且对所提出的方案进行了理论的分析和数值仿真实验。本论文的研究思路是:按照主用户(有频谱利用授权用户或者任何先占用非授权频谱资源的用户)的频谱应用规律或者特性设计获取频谱“空洞”的方案并在其上进行数据通信。为了进行比较,本论文采用了两种主用户频谱应用规律,即在不同的时隙主用户出现的规律服从独立同分布(identically independently distribution,i.d.d.)模型和两个状态的Markov模型(2-state Markov model)。在本论文所提出用于机会频谱接入的持续度可调感知方法中,在每一个时隙中对感兴趣的频段进行感知,判断是否有‘空洞',如果没有,作以下操作:达到一定重复次数后在下一个时隙随机地跳到另一个感兴趣的频段继续感知,否则在所在的频段继续进行频谱感知;如果找到了频谱空洞,则认为频谱接入成功并可以传输数据。一旦主用户出现在某个频段上,次用户必须立刻放弃使用这个频段。不同重复次数(T)的设置导致不同的方案:(1)当T为无穷大时,方案称为永久持续感知单个频段;(2)当1＜T＜∞时,方案为持续T个时隙感知单个频段再随机地跳换频段;(3)而T=1时,在每个时隙中随机地跳换感知的频段。为了对所提出的方案进行评估,本论文采用频谱利用效率和频谱接入延时作为评估参数。通过理论分析和计算机仿真得出:1)两个状态的Markov模型比i.d.d.模型更能反映主用户频谱使用规律;2)主用户的出现概率越低,次用户的频谱利用率越高,延时越小;3)主用户的出现概率越高,次用户的频谱利用率越低,延时越大;4)在两个状态的Markov模型下仿真和分析结果显示出方案(3)最好,(2)其次,而(1)最差。所提出用于机会频谱接入的持续度可调感知方法可以提高频谱的利用率,特别适合用于军事或者应急(如地震)的紧急频谱需求环境。
创新点三:针对机会频谱接入方式提出了基于统计的功率和子频段分配方案。主要创新点包括:(a)针对认知无线电中的二维不确定性环境(频谱资源和信道衰落的不确定性)建立了数学模型。该问题被简化成凸优化问题,这样就可以得到全局最优方案以及高效率的资源分配算法。该算法可以实现功率和子频段分配,并允许用户的直接参与,即用户能依据个人需要输入个性化的参数;(b)所提出的算法对不确定的认知无线环境可以提供鲁棒的服务质量保证;(c)可以实现软频谱感知。更详细的介绍在下一段。
文献调研显示目前大部分的认知无线电的研究工作是围绕着频谱的再利用方法,正如创新点二中的研究内容,而涉及资源分配问题的比较少见。由于现有的频谱资源是固定分配的,传统的无线资源分配问题研究在建模的时候一般不考虑频谱资源是否存在,而只考虑无线信道衰落的不确定性。在认知无线电中的资源分配问题则不然,即除了考虑信道衰落的不确定性以外,还得考虑频谱资源存在的不确定性。同时考虑二维不确定性因素对建立资源分配数学模型所增加的难度是可想而知的。此时,资源分配的目标是在环境的不确定情况下找到一个功率分配方案以达到某服务质量指标的保证。所描述的问题跟投资管理(Portfolio Optimization)所解决的问题是类似的。作为一个类比,可以把频段视为“股票”,功率分配就好比要投资的基会。投资者(认知无线电设备)的目标是尽可能降低投资风险,而风险是由回报率的方差衡量的。本论文的研究思路是采用投资管理模型来解决二维不确定性下的资源分配问题。在多因素不确定或者“高风险”的认知无线电环境中的目标是降低传输率的方差来达到稳定的数据率。本文建立的数学模型基于信道增益数据,求功率分配向量以降低传输率的方差。由于传输率和功率是基于香农定理的对数关系,因此建模的障碍在于简化传输率(功率的对数方程)的方差表达式。本论文采用了δ近似法(δ-Approximation)来得到简单的传输率方差的表达式,而且在所得到的表达式中。可以直接利用过去时隙的信道增益数据求解。基于上述的简化表达,本论文针对不同用户偏好,建立了两个优化目标函数;即(1)用户只考虑降低数据率方差;(2)用户同时考虑降低数据率方差和提高数据率均值(这个数学模型允许用户直接输入个性化的参数,即表示用户对不良QoS条件的承受度)。由于所建立的的数学模型是二次规划(Quadratic Programming)的凸优化问题,本论文得到了全局最优功率分配方案以及高效率的算法。从应用的角度,方案可以提供鲁棒的服务质量保证,这对”高风险”认知无线电环境很关键;可以同时实现功率分配和频谱接入;可以让用户直接输入个性化的参数如数据率以及实现软频谱检测。为了了解方案中的关键参数即最大功率和用户承受度的局限性,本论文还对所得到的方案进行了敏感度分析(Sensitivity Analysis),结果显示不同的参数设计或者选择会导致不同QoS效果,因此实际应用的时候该参数的设计将取决于应用环境和用户爱好的因素来确定。由于采用以前的信道增益数据做出软频谱检测,因此跟创新点二比较,这部分的工作更适用于民用的频谱再利用情景。未来的工作可以就认知无线电在二维不确定性中的资源分配问题进行鲁棒性研究。
创新点四:针对收费的频谱使用方式,提出一个层次化的动态频谱转让框架(Hierarchical Dynamic Spectrum Leasing Framework),以及基于该框架的定价方案。
目前,很多国家的有关频谱管理政策机构正在考虑,把大部分的频谱资源的分配从静态的固定模式转化到灵活的动态频谱转让机制。为了成功地实现动态频谱转让机制,需要全局性地考虑多方面的因素,首先是频谱利用率的最大化和管理的方便性,其次是无线通信服务运营商的利益最大化,最后是普通用户的功率控制和服务质量保证。针对这个情景,本论文提出一个层次化的动态频谱转让框架。该框架中,频谱管理者(Spectrum Manager,SM)是最高层:他负责实现使用效益最高的频谱分配。SM有频段需求量估计分析和频段定价两个功能模块。由于机制是基于频谱转让,SM的重要工作是对每个频段按照需求量分析的结果进行价值评估并定价,以达到频谱使用价值最大化的目的。在本论文中,SM采用垄断的定价方式。SM通过收集频谱的需求信息来确定价格,这样频谱的价格动态地随着需求量的变化而变,实时地反映频谱的价值。框架的中间层是网络运营商(Network Operator,NetOp):它按照SM提出的频谱价格和用户对其服务的需求量,来规划自己的网络,选择使用的技术,以及服务的收费。服务提供商关心的则是把自己的利益最大化。他是通过收服务费来获利。框架的最底层是最终用户(End User):用户关心的是以最便宜的方式得到最好的服务质量。他的发射功率和频谱(或者服务)受价格的约束。
创新点五:针对基于价格的功率和子频段分配,本文:(1)对SM和网络运营商建立了回报率最大化的数学模型,提出了解决方法;(2)针对最终用户,(a)建立了基于非合作博弈的功率分配数学模型,并证明了功率控制策略的均衡点的存在和唯一性;(b)提出考虑和不考虑服务质量保证的,基于频谱价格的功率和子频段分配两个算法。更详细的介绍在下一段。
为了达到全局性的最有效的资源分配方法,本文提出具体的SM和NetOp的利益最大化数学模型。在此,对SM和NetOp的优化感兴趣的结果是给最终用户提供频段价格的参数。该频段价格参数将被最终用户使用,并用于功率和子频段的分配。由于用户在层次化框架的最底层,而发射功率和频谱(或者服务)受上层所定的频段价格的约束,他的资源的分配方式则适合使用非合作的博弈描述。因此,本论文建立了基于非合作博弈的功率和子频段分配的数学模型。每一个用户的效用函数是由Sigmoid函数和频段价格而形成准凹效用函数(Quasi-concaveutility function)。对于该博弈,本文证明了功率控制策略的均衡点的存在和唯一性。针对该基于频谱价格的功率控制和子频段分配策略,提出两个算法,一个考虑服务质量,另一个不考虑服务质量。数值仿真结果显示,所提出的方案将对频谱分配、网络运营商利益最大化,并可有效的进行功率和子频段分配。
基于认知无线电技术,以上的创新点有助于短期和长期的进行灵活高效率的频谱分配,从而达到最大的频谱使用效益,并且系统中的功率和子频段分配将以最低代价达到最好的服务质量。
The fixedness of the current Command and Control spectrum allocation approach has lead to an artificial scarcity of spectrum due to under utilization;which in turn affect the optimization of the whole scope of wireless communication resources.A radio,therefore,that can sense and be aware of its radio spectrum environment and available services;to identify vacant spectrum resources and use it freely or at a price, has the potential to provide higher bandwidth services,improve spectrum efficiency, and reduce inefficiency due to the human factor in spectrum management.This could be achieved by a radio that can make autonomous decisions about how it utilizes radio resources at its disposal.Cognitive radios are strong candidates for achieving this.
In this thesis,we present several resource allocation methodologies in cognitive radios,primarily in the context of efficient spectrum resource allocation and power control,to more efficiently support flexibility and optimization in wireless networks. Our theoretical modeling provides guidelines for achieving Pareto optimal spectrum allocation and characterizes optimization problems under uncertain resource availability constraints.Our numerical and simulations results demonstrate significant improvement in spectral resources utilization efficiency and acquisition delay,robust Quality of Service,and budget efficient power-control strategies.
Resource allocation in cognitive radios is investigated under two schemes:the free spectrum usage,Opportunistic Spectrum Access scheme,under the current Command and Control regime,and the paid spectrum usage,Dynamic Spectrum Leasing.For the Opportunistic Spectrum Access,where the secondary spectrum user searches for spectrum holes which are not used by primary users(license holders) and communicate through them,we propose variable-persistence spectrum recovery schemes.A key feature of the proposed methods is that insight about the spectrum recovery strategy and development of cognitive radio systems is gained.The variable-persistence schemes are analyzed under the identically independently distribution(i.i.d.) and the 2-state Markov primary user traffic patterns.Numerical case studies are presented to verify the theoretical analysis and illustrate the performance of the schemes proposed.
Successful resource allocation in cognitive radio systems operating under opportunistic spectrum usage has to overcome the uncertainty of spectrum bands availability as well as the chaotic wireless propagation environment.One of the highlights of this work is the application of the concept of portfolio optimization to characterize the joint power control and spectrum band discovery problems under uncertain cognitive radio operative environments.The resulting power strategy also marks out the subbands to be used-essentially achieving soft spectrum sensing.The limitations of the strategies are investigated through the sensitivity analysis of the solutions obtained.A raw data processing approach is also given leading to an alternative algorithm for stable data processing.Numerical results are presented to demonstrate the potential of the proposed approaches.
As for paid spectrum usage,this work proposes a hierarchical framework for dynamic spectrum leasing.A centralized spectrum price setting mechanism to facilitate the framework is also proposed.Further,the advantages and social optimality of the framework are analyzed.Based on that,we holistically develop a mechanism that enables joint spectrum allocation,revenue maximization and power control through spectrum pricing while achieving a desired QoS performance.This lets the Spectrum Manager (SM) to maximize the spectrum usage efficiency through monopolistic based price setting;the Network Operator(NetOp) to maximize its revenue;and the end user(s) to autonomously trade-off between its utility and spectrum cost through emission power control-essentially forming a non-cooperative power control game for which we show the existence and uniqueness of the Nash equilibrium.Numerical results are presented to demonstrate the potential of the proposed framework in the spectrum price setting by the SM,revenue maximization by the NetOp,and power control strategy adopted by the user in various price thresholds.
在很多国家,大量的可用频谱资源已被分配。尽管如此,美国联邦通信委员会(FCC)频谱策略任务工作报告指出,在很多个地区的大部分时间段,多数的频谱都处于空闲状态,这说明频谱的缺乏主要是由频谱分配机制引起的而不是真正意义上的频谱缺乏。在现有的固定式频谱分配方式中,频谱授权只能在固定的地方、用特定的技术、提供特定的无线服务,并且不允许转让使用权或者更换技术,从而导致了很低的频谱资源利用率。
对于只关心服务而不关心技术的用户而言,网络运行商在保证他们的利益最大化的前提之下,应该为用户所需的服务提供最适合的技术。然而,目前的情况是用户只能得到运行商提供的某些特定服务,并用这些服务会受到现有技术的限制以及频谱管理政策的约束。为了更好地适应用户的需求的变化,网络以及终端应该在服务类型、技术和频谱应该等方面具有更高的灵活性。
在2000年,Joseph MitolaⅢ提出了认知无线电(Cognitive Radio,CR)的概念。认知无线电是可以感知外界通信环境的智能通信系统。通过认知无线电技术可以动态地增加网络和个人用户的可用频谱总数,从而为频谱分配提供了一个可能的解决方案,同时可为网络及其终端提供更高的灵活度。因而对无线网络资源分配的研究具有重大意义。
本论文对基于认知无线电的频谱资源分配开展了研究。为了达到最大频谱使用效益,频谱资源需要合理的动态分配。研究的内容包括:(1)首先是如何分配频谱才能达到最大使用效益的问题。主要内容是研究频谱分配方式的统一理论框架。(2)从短期频谱需求来看,要提出并分析从现有的固定频谱分配的环境中免费地获取频谱资源即机会性频谱接入的方法(Opportunistic SpectrumAccess)。(3)在基于机会性频谱接入方式下如何进行功率和子频段分配。主要研究基于统计的功率和子频段分配以及软频谱获取问题。(4)从长期频谱需求来看,要提出并分析基于收费的动态频谱转让(Dynamic Spectrum Leasing)机制。先提出能基于费用频谱交换的框架,然后研究频谱定价方法以及方法的最优性分析。(5)在基于动态频谱转让方式下进行功率和子频段分配。主要研究基于价格的功率和子频段分配问题。
本论文的主要创新点包括:
创新点一:针对不同的频谱分配模式提出了统一的理论研究框架,在该框架之下,动态的频谱分配方式允许频谱使用权的转让,因而能带来最大的使用效用,是最优的分配方式,固定的分配方式为其次,而自由的频谱使用为最次。更详细的介绍在下两段。
虽然多方提倡基于市场的动态频谱分配机制,但是,在现有的文献并没有从理论的角度给出统一的理论研究框架。所谓统一的理论研究框架是指能用于研究固定分配模式(Command and Control)、动态频谱转让(Dynamic SpectrumLeasing)以及自由共享(Spectrum Commons)的频谱分配机制。本论文针对这三种频谱分配机制提出了统一的理论研究框架,从而分析了它们对频谱使用所带来的效益。该框架基于一个明显的事实,即由于频谱管理一般是由政府机关来控制,因此在某种意义上可以认为是国家垄断的一项业务,而把对某频段使用授权视为垄断的产品。因此,本论文的研究思路是采取耐用品垄断(Durable GoodsMonopoly)理论来建立该框架。基于此就可以得到一个比较以上三种频谱分配方式的框架。该框架指出动态的(允许频谱使用权的转让)频谱分配方式为最优(能带来最大的使用效益),固定的分配方式为其次,而自由的频谱使用为最次。本文提出对基于价格的频谱分配而言,达到帕累托最优(Pareto Optimality)的条件是频谱管理者可以知道每个用户所愿意付出的代价,并因此选出能出最高的频谱价化转让给它使用。帕累托最优是指资源分配的一种状态,在不使任何人境况变坏的情况下,无法再使某些人的处境变好。
上面的理论框架跟部分国家的未来频谱管理计划吻合。英国通信办公室(OFCOM)的未来频谱分配计划显示:现在固定的频谱使用机制占96%,而2010年以后只占22%;动态的频谱转让机制目前占0%,而将来占71%;自由的频谱使用机制目前占4%,而将来占7%。美国的FCC以及其他国家如新西兰、澳大利亚、中国台北等也都提出了类似的趋势,即频谱的动态转让机制。从长期发展来看,我们应该抓紧研究可以灵活地获得频谱资源的通信技术,否则到时候技术落后于政策将导致失去'先下手为强'的优势。从实际情况来看,大部分国家的政策还不支持灵活的频谱分配,所以作为一个短期的方案还是需要考虑如何从现有的固定的频谱分配机制中获取免费的频谱资源。
本论文考虑了这两种情况,即认知无线电技术中,如何在固定(免费)和动态(收费)频谱分配机制下进行资源分配。所考虑的资源为政策中的频谱分配和系统中的功率和子频段分配。基于以上的分析比较结果,本文进而围绕着频谱免费和收费两种情况进行频谱、功率和子频段资源分配的研究:针对免费频谱获取,研究基于机会频谱接入方式以及基于统计的软频谱获取,功率和子频段分配;针对收费的频谱分配研究是以动态频谱转让方式和基于价格的功率和子频段分配来实现的。
创新点二:针对固定的频谱分配方式中的免费频谱获取,提出了用于机会频谱接入的持续可调(Variable Persistence)的感知方法,以填满频谱空洞并提高频谱的利用率。具体提出了三个频谱感知方案,并用对所提出的方案进行了理论分析和数值仿真实验。
由于固定分配机制频谱资源利用的不灵活,导致了很低的频谱利用效率。这部分在解决如何最有效地再利用频谱资源?这一问题。针对固定的频谱分配方式,提出了用于机会频谱接入的持续可调的感知方法,以填满频谱'空洞'并提高频谱的利用率。持续度可调是指感知某一个子频段的时隙数可以调整,而频谱空洞是指那些已分配给授权用户使用,但是在特定的时间和地点授权用户却没有使用的频段。具体提出了三个频谱感知方案,并且对所提出的方案进行了理论的分析和数值仿真实验。本论文的研究思路是:按照主用户(有频谱利用授权用户或者任何先占用非授权频谱资源的用户)的频谱应用规律或者特性设计获取频谱“空洞”的方案并在其上进行数据通信。为了进行比较,本论文采用了两种主用户频谱应用规律,即在不同的时隙主用户出现的规律服从独立同分布(identically independently distribution,i.d.d.)模型和两个状态的Markov模型(2-state Markov model)。在本论文所提出用于机会频谱接入的持续度可调感知方法中,在每一个时隙中对感兴趣的频段进行感知,判断是否有‘空洞',如果没有,作以下操作:达到一定重复次数后在下一个时隙随机地跳到另一个感兴趣的频段继续感知,否则在所在的频段继续进行频谱感知;如果找到了频谱空洞,则认为频谱接入成功并可以传输数据。一旦主用户出现在某个频段上,次用户必须立刻放弃使用这个频段。不同重复次数(T)的设置导致不同的方案:(1)当T为无穷大时,方案称为永久持续感知单个频段;(2)当1＜T＜∞时,方案为持续T个时隙感知单个频段再随机地跳换频段;(3)而T=1时,在每个时隙中随机地跳换感知的频段。为了对所提出的方案进行评估,本论文采用频谱利用效率和频谱接入延时作为评估参数。通过理论分析和计算机仿真得出:1)两个状态的Markov模型比i.d.d.模型更能反映主用户频谱使用规律;2)主用户的出现概率越低,次用户的频谱利用率越高,延时越小;3)主用户的出现概率越高,次用户的频谱利用率越低,延时越大;4)在两个状态的Markov模型下仿真和分析结果显示出方案(3)最好,(2)其次,而(1)最差。所提出用于机会频谱接入的持续度可调感知方法可以提高频谱的利用率,特别适合用于军事或者应急(如地震)的紧急频谱需求环境。
创新点三:针对机会频谱接入方式提出了基于统计的功率和子频段分配方案。主要创新点包括:(a)针对认知无线电中的二维不确定性环境(频谱资源和信道衰落的不确定性)建立了数学模型。该问题被简化成凸优化问题,这样就可以得到全局最优方案以及高效率的资源分配算法。该算法可以实现功率和子频段分配,并允许用户的直接参与,即用户能依据个人需要输入个性化的参数;(b)所提出的算法对不确定的认知无线环境可以提供鲁棒的服务质量保证;(c)可以实现软频谱感知。更详细的介绍在下一段。
文献调研显示目前大部分的认知无线电的研究工作是围绕着频谱的再利用方法,正如创新点二中的研究内容,而涉及资源分配问题的比较少见。由于现有的频谱资源是固定分配的,传统的无线资源分配问题研究在建模的时候一般不考虑频谱资源是否存在,而只考虑无线信道衰落的不确定性。在认知无线电中的资源分配问题则不然,即除了考虑信道衰落的不确定性以外,还得考虑频谱资源存在的不确定性。同时考虑二维不确定性因素对建立资源分配数学模型所增加的难度是可想而知的。此时,资源分配的目标是在环境的不确定情况下找到一个功率分配方案以达到某服务质量指标的保证。所描述的问题跟投资管理(Portfolio Optimization)所解决的问题是类似的。作为一个类比,可以把频段视为“股票”,功率分配就好比要投资的基会。投资者(认知无线电设备)的目标是尽可能降低投资风险,而风险是由回报率的方差衡量的。本论文的研究思路是采用投资管理模型来解决二维不确定性下的资源分配问题。在多因素不确定或者“高风险”的认知无线电环境中的目标是降低传输率的方差来达到稳定的数据率。本文建立的数学模型基于信道增益数据,求功率分配向量以降低传输率的方差。由于传输率和功率是基于香农定理的对数关系,因此建模的障碍在于简化传输率(功率的对数方程)的方差表达式。本论文采用了δ近似法(δ-Approximation)来得到简单的传输率方差的表达式,而且在所得到的表达式中。可以直接利用过去时隙的信道增益数据求解。基于上述的简化表达,本论文针对不同用户偏好,建立了两个优化目标函数;即(1)用户只考虑降低数据率方差;(2)用户同时考虑降低数据率方差和提高数据率均值(这个数学模型允许用户直接输入个性化的参数,即表示用户对不良QoS条件的承受度)。由于所建立的的数学模型是二次规划(Quadratic Programming)的凸优化问题,本论文得到了全局最优功率分配方案以及高效率的算法。从应用的角度,方案可以提供鲁棒的服务质量保证,这对”高风险”认知无线电环境很关键;可以同时实现功率分配和频谱接入;可以让用户直接输入个性化的参数如数据率以及实现软频谱检测。为了了解方案中的关键参数即最大功率和用户承受度的局限性,本论文还对所得到的方案进行了敏感度分析(Sensitivity Analysis),结果显示不同的参数设计或者选择会导致不同QoS效果,因此实际应用的时候该参数的设计将取决于应用环境和用户爱好的因素来确定。由于采用以前的信道增益数据做出软频谱检测,因此跟创新点二比较,这部分的工作更适用于民用的频谱再利用情景。未来的工作可以就认知无线电在二维不确定性中的资源分配问题进行鲁棒性研究。
创新点四:针对收费的频谱使用方式,提出一个层次化的动态频谱转让框架(Hierarchical Dynamic Spectrum Leasing Framework),以及基于该框架的定价方案。
目前,很多国家的有关频谱管理政策机构正在考虑,把大部分的频谱资源的分配从静态的固定模式转化到灵活的动态频谱转让机制。为了成功地实现动态频谱转让机制,需要全局性地考虑多方面的因素,首先是频谱利用率的最大化和管理的方便性,其次是无线通信服务运营商的利益最大化,最后是普通用户的功率控制和服务质量保证。针对这个情景,本论文提出一个层次化的动态频谱转让框架。该框架中,频谱管理者(Spectrum Manager,SM)是最高层:他负责实现使用效益最高的频谱分配。SM有频段需求量估计分析和频段定价两个功能模块。由于机制是基于频谱转让,SM的重要工作是对每个频段按照需求量分析的结果进行价值评估并定价,以达到频谱使用价值最大化的目的。在本论文中,SM采用垄断的定价方式。SM通过收集频谱的需求信息来确定价格,这样频谱的价格动态地随着需求量的变化而变,实时地反映频谱的价值。框架的中间层是网络运营商(Network Operator,NetOp):它按照SM提出的频谱价格和用户对其服务的需求量,来规划自己的网络,选择使用的技术,以及服务的收费。服务提供商关心的则是把自己的利益最大化。他是通过收服务费来获利。框架的最底层是最终用户(End User):用户关心的是以最便宜的方式得到最好的服务质量。他的发射功率和频谱(或者服务)受价格的约束。
创新点五:针对基于价格的功率和子频段分配,本文:(1)对SM和网络运营商建立了回报率最大化的数学模型,提出了解决方法;(2)针对最终用户,(a)建立了基于非合作博弈的功率分配数学模型,并证明了功率控制策略的均衡点的存在和唯一性;(b)提出考虑和不考虑服务质量保证的,基于频谱价格的功率和子频段分配两个算法。更详细的介绍在下一段。
为了达到全局性的最有效的资源分配方法,本文提出具体的SM和NetOp的利益最大化数学模型。在此,对SM和NetOp的优化感兴趣的结果是给最终用户提供频段价格的参数。该频段价格参数将被最终用户使用,并用于功率和子频段的分配。由于用户在层次化框架的最底层,而发射功率和频谱(或者服务)受上层所定的频段价格的约束,他的资源的分配方式则适合使用非合作的博弈描述。因此,本论文建立了基于非合作博弈的功率和子频段分配的数学模型。每一个用户的效用函数是由Sigmoid函数和频段价格而形成准凹效用函数(Quasi-concaveutility function)。对于该博弈,本文证明了功率控制策略的均衡点的存在和唯一性。针对该基于频谱价格的功率控制和子频段分配策略,提出两个算法,一个考虑服务质量,另一个不考虑服务质量。数值仿真结果显示,所提出的方案将对频谱分配、网络运营商利益最大化,并可有效的进行功率和子频段分配。
基于认知无线电技术,以上的创新点有助于短期和长期的进行灵活高效率的频谱分配,从而达到最大的频谱使用效益,并且系统中的功率和子频段分配将以最低代价达到最好的服务质量。
The fixedness of the current Command and Control spectrum allocation approach has lead to an artificial scarcity of spectrum due to under utilization;which in turn affect the optimization of the whole scope of wireless communication resources.A radio,therefore,that can sense and be aware of its radio spectrum environment and available services;to identify vacant spectrum resources and use it freely or at a price, has the potential to provide higher bandwidth services,improve spectrum efficiency, and reduce inefficiency due to the human factor in spectrum management.This could be achieved by a radio that can make autonomous decisions about how it utilizes radio resources at its disposal.Cognitive radios are strong candidates for achieving this.
In this thesis,we present several resource allocation methodologies in cognitive radios,primarily in the context of efficient spectrum resource allocation and power control,to more efficiently support flexibility and optimization in wireless networks. Our theoretical modeling provides guidelines for achieving Pareto optimal spectrum allocation and characterizes optimization problems under uncertain resource availability constraints.Our numerical and simulations results demonstrate significant improvement in spectral resources utilization efficiency and acquisition delay,robust Quality of Service,and budget efficient power-control strategies.
Resource allocation in cognitive radios is investigated under two schemes:the free spectrum usage,Opportunistic Spectrum Access scheme,under the current Command and Control regime,and the paid spectrum usage,Dynamic Spectrum Leasing.For the Opportunistic Spectrum Access,where the secondary spectrum user searches for spectrum holes which are not used by primary users(license holders) and communicate through them,we propose variable-persistence spectrum recovery schemes.A key feature of the proposed methods is that insight about the spectrum recovery strategy and development of cognitive radio systems is gained.The variable-persistence schemes are analyzed under the identically independently distribution(i.i.d.) and the 2-state Markov primary user traffic patterns.Numerical case studies are presented to verify the theoretical analysis and illustrate the performance of the schemes proposed.
Successful resource allocation in cognitive radio systems operating under opportunistic spectrum usage has to overcome the uncertainty of spectrum bands availability as well as the chaotic wireless propagation environment.One of the highlights of this work is the application of the concept of portfolio optimization to characterize the joint power control and spectrum band discovery problems under uncertain cognitive radio operative environments.The resulting power strategy also marks out the subbands to be used-essentially achieving soft spectrum sensing.The limitations of the strategies are investigated through the sensitivity analysis of the solutions obtained.A raw data processing approach is also given leading to an alternative algorithm for stable data processing.Numerical results are presented to demonstrate the potential of the proposed approaches.
As for paid spectrum usage,this work proposes a hierarchical framework for dynamic spectrum leasing.A centralized spectrum price setting mechanism to facilitate the framework is also proposed.Further,the advantages and social optimality of the framework are analyzed.Based on that,we holistically develop a mechanism that enables joint spectrum allocation,revenue maximization and power control through spectrum pricing while achieving a desired QoS performance.This lets the Spectrum Manager (SM) to maximize the spectrum usage efficiency through monopolistic based price setting;the Network Operator(NetOp) to maximize its revenue;and the end user(s) to autonomously trade-off between its utility and spectrum cost through emission power control-essentially forming a non-cooperative power control game for which we show the existence and uniqueness of the Nash equilibrium.Numerical results are presented to demonstrate the potential of the proposed framework in the spectrum price setting by the SM,revenue maximization by the NetOp,and power control strategy adopted by the user in various price thresholds.
引文
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[49] Berger R J. Open Spectrum: A Path to Ubiquitous Connectivity. ACM Queue, 2003,1(3):60-68.
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[2] Neel J O. Analysis and Design of Cognitive Radio Networks and Distributed Radio Resource Management Algorithms[D]. Virginia Tech, Sept., 2006.
[3] Mitola-III J, Maguire-Jr G Q. Cognitive Radio: Making Software Radios More Personal.IEEE Personal Communications, 1999, 6:13-18.
[4] Coase R H. The Federal Communications Commission. Journal of Law and Economics,1959,2:1-40.
[5] ITU. Spectrum Pricing Study, Communications Study Group, 1993.
[6] Kruse J. Competition in Mobile Communications and the Allocation of Scarce Resources: The Case of UMTS. Proceedings of The Economics of Antitrust in the Telecommunications Sector, 2002.
[7] Butler J M, Webb W T. A Implementation of Spectrum Usage Rights for Liberalization of the Radio Spectrum. J. of Commun. and Networks, 2006, 8(2):163-168.
[8] Cave M, Doyle C, Webb W. Essentials of Modern Spectrum Management. Cambridge University Press, 2007.
[9] Miller N. Notes on Microeconomics Theory: Chapter 9. Aug., 2006.
[10] Lehr W, Crowcroft J. Managing Shared Access to a Spectrum Commons. Proc. IEEE DySPAN 2005,2005. 420-444.
[11] Cheong Y W, Cheng R S, Letaief K B, et al. Multiuser OFDM and adaptive subcarrier, bit, and power allocation. IEEE J. Select. Areas Commun., 1999, 17:1747-1758.
[12] Sternad M, Svensson T, Ottosson T, et al. Towards Systems Beyond 3G Based on Adaptive OFDMA Transmission. Proceedings of IEEE, 2007, 95(12):2432-2455.
[13] FCC. Review of Spectrum Management Practice, Dec, 2003.http://hraunfoosl.fcc.gov/edocs_public/attachement/DOC-229047Al.pdf.
[14] Noam E. Spectrum Auctions: Yesterday's Heresy, Today's Orthodoxy, Tomorrow's Anachronism. Taking the Next Step to Open Spectrum Access. J. Law and Economics,1998,41(2):765-790.
[15] Zhao Q, Sadler B M. A Survey of Dynamic Spectrum Access. IEEE Signal Processing Magazine, 2007,24(3):79-89.
[16] Ileri (?), Mandayam N B. Dynamic Spectrum Access Models: Toward an Engineering Perspective in the Spectrum Debate. IEEE Commun. Mag., 2008, 46(1):153—160.
[17] Ting C, Wildman S S, Bauer J M. Comparing Welfare for Spectrum Property and Spectrum Commons Governance Regimes. Telecommunications Policy, 2005, 29:711-730.
[18] Peha J M. Approaches to spectrum sharing. IEEE Communications Magazine., 2005. 10-12.
[19] Weiss T, Jondral F. Spectrum pooling: An innovative strategy for the enhancement of spectrum efficiency. IEEE Commun. Mag., 2004, 42:8-14.
[20] Haykin S. Cognitive radio: brain-empowered wireless communications. IEEE J. Select.Areas Commun., 2005, 23(2):201-220.
[21] Akyildiz I F, Lee W Y, Vuran M C, et al. NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput. Networks, 2006, 50(13):2127-2159.
[22] Zhao Q, Tong L, Swami A. Decentralized cognitive MAC for dynamic spectrum access.Proceedings of Proc. IEEE DySPAN, 2005. 224-232.
[23] Capar F, Martoyo I, Weiss T, et al. Comparison of bandwidth utilization for controlled and uncontrolled channel assignment in a spectrum pooling system. Proceedings of Proc. IEEE VTC Spring'02, volume 3, 2002. 1069-1073.
[24] Mangold S, ZZhong, Challapali K, et al. Spectrum agile radio: radio resource measurements for opportunistic spectrum usage. Proceedings of Proc. IEEE GLOBECOM'04, volume 6,2004. 3467-3471.
[25] Cordeiro C, Challapali K, Birru D, et al. IEEE 802.22: the first worldwide wireless standard based on cognitive radios. Proceedings of Proc. IEEE DySPAN, 2005. 328-337.
[26] Xing Y, Chandramouli R, Mangold S, et al. Dynamic Spectrum Access in Open Spectrum Wireless Networks. IEEE J. Select. Areas Commun., 2005,24(3):626-637.
[27] Chen Y, Q Zhao A S. Distributed Cognitive MAC for Energy-Constrained Opportunistic Spectrum Access. Proceedings of Proc. IEEE MILCOM'06, 2006. 1-7.
[28] Zhang Q, Kokkeler A, Smit G. An Efficient FFT for OFDM based Cognitive Radio on a Reconfigurable Architecture. Proceedings of Proc. IEEE ICC, 2007.
[29] Bansal G, Hossain J, Bhargava V K. Adaptive Power Loading for OFDM-based Cognitive Radio Systems. Proceedings of Proc. IEEE ICC, 2007.
[30] Devroye N, Tarokh V. Fundamental Limits of Cognitive Radio Networks. In: Fitzek F H P, Katz M, (eds.). Proceedings of Cognitive Wireless Networks: Concepts, Methodologies and Vision. Springer, 2007: 327-351.
[31] Buddhikot M M. Understanding Dynamic Spectrum Access: Models, Taxonomy and Challenges. Proceedings of Proc. IEEE DySPAN, 2007.
[32] Etkin R, Parekh A, Tse D. Spectrum Sharing for Unlicensed Bands. IEEE J. Sel. Areas Commun., 2007, 25(3):517-528.
[33] Scutari G, Palomar D P, Barbarossa S. Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory-Part I: Nash Equilibria. IEEE Trans.Signal Processing, 2008, 56(3): 1230-1249.
[34] Wang F, Krunz M, Cui S. Price-Based Spectrum Management in Cognitive Radio Networks.IEEE J. Sel. Topics in Signal Processing, 2008,2(1):74-87.
[35] Bykowsky M, Olson M, Sharkey W. A Market-Based Approach to Establishing Licensing Rules: Licensed versus Unlicensed Use of Spectrum, Feb., 2008.hhttp://www.fcc.gov/osp/workingp.html.
[36] Simeone O, Stanojev I, Savazzi S, et al. Spectrum Leasing for Cooperative Secondary Ad Hoc Networks. IEEE J. Sel. Areas Commun., 2008,26(1):1-11.
[37] Niyato D, Hossain E. Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion. IEEE J. Sel.Areas Commun., 2008, 26(1):192-202.
[38] Feng N, Mau S C, Mandayam N B. Pricing and Power Control for Joint Network-Centric and user-Centric Radio Resource Management. IEEE Trans. on Commun., 2004,52(9): 1547-1557.
[39] Feng N, Mau S C, MandayamNB. Joint Network-Centric and User-Centric Radio Resource Management in a Multicell System. IEEE Trans, on Commun., 2005, 53(7):1114-1118.
[40] Kelly F, Maulloo A, Tan D. Rate control in communication networks:Shadow prices, proportional fairness, and stability. J. Oper. Res. Soc, 1998, 49:237 - 252.
[41] Ast(?)ly D, Dahlman E, Frenger P, et al. A Future Radio-Access Framework. IEEE J. Select.Areas Commun., 2006, 24(3):693-706.
[42] Desoli G, Filippi E. An Outlook on the Evolution of Mobile Terminals: From Monolithic to Multi-Radio, Multi-Application Platforms. IEEE Circuits and Systems Magazine, 2006,(11):17-29.
[43] Dagres I, Zalonis A, Dimitriou N, et al. Flexible Radio: a Framework for Optimized Multimodal Operation via Dynamic Signal Design. EURASIP Journal on Wireless Communications and Networking, 2005. 284-297.
[44] Jondral F K. Software-Defined Radio-Basics and Evolution to Cognitive Radio.EURASIP Journal on Wireless Communications and Networking, 2005, (3):275-283.
[45] Zheng H, Cao L. Device-centric spectrum management. Proceedings of Proc. IEEE DyS-PAN, 2005. 56-65.
[46] Martens M A, Shelanski H A. Between Two Utopias: The Economic and Technological Foundations of the Property Rights and Commons Models for Spectrum Policy. Proceedings of Technology and Policy for Advanced Spectrum (TAPAS'06), Boston, MA, 2006.
[47] Nekovee M. Dynamic spectrum access - concepts and future architectures. BT Technology Journal, 2006, 24(2): 111-116.
[48] Yu H C, Lee Z Y, Lee H Y. Revising Taiwan's frequency usage fee regulation. Telecommunications Policy, 2004,28:679-695.
[49] Berger R J. Open Spectrum: A Path to Ubiquitous Connectivity. ACM Queue, 2003,1(3):60-68.
[50] Kloeck C, Jaekel H, Jondral F. Multi-Agent Radio Resource Allocation. Mobile Networks Applications, 2006, 11(6):813-824.
[51] Maill(?) P, Tuffin B. Pricing the Internet With Multibid Auctions. IEEE/ACM Trans. on Networking, 2006, 14(5):992-1004.
[52] Shenker S. Fundamental design issues for the future internet. IEEE J. Sel. Areas Commun.,1995, 13(7):1176-1188.
[53] Song G, Li Y G. Utility-Based Resource Allocation and Scheduling in OFDM-Based Wireless Broadband Networks. IEEE Commun. Magazine, 2005, 43(12):127-134.
[54] Riihij(?)rvi J, Wellens M, M(?)h(?)nen P. Link-Layer Abstractions for Utility-Based Optimization in Cognitive Wireless Networks. Proceedings of Proc. CrownCom, 2006.
[55] Odlyzko A. Data networks are lightly utilized, and will stay that way. Review of Network Economics, 2003,2(3):210-237.
[56] Fudenberg D, Tirole J. Game Theory. Cambridge, MA: MIT Press, 1991.
[57] Engels M. Portfolio Optimization: Beyond Markowitz[M]. Leiden University, Jan., 2004.
[58] Markowitz H. Portfolio Selection. Journal of Finance, 1952, 7(1):77—91.
[59] Boyd S P, Vandenberghe L. Convex Optimization. Cambridge University Press, 2004.
[60] Luo Z Q, Yu W. An Introduction to Convex Optimization for Communications and Signal Processing. IEEE J. Sel. Areas Commun., 2006,24(8):1426-1438.
[61] Lobo M S, Fazel M, Boyd S. Portfolio Optimization with Linear and Fixed Transaction Cost. Annals of Operation Research, 2007, 152(1):341-365.
[62] Mitola-III J. Cognitive Radio Architecture: The Engineering Foundations of Radio XML.Wiley, 2006.
[63] Rasmusen E. Games and Information: And Introduction to Game Theory. Cambridge University Press, 1990.
[64] Choi Y J, Park S, Bahk S. Multichannel random access in OFDMA wireless networks.IEEE J. Select. Areas Commun., 2006,24(3):603-613.
[65] P Pawelczak R V P, Hakmat R. Opportunistic Spectrum Multichannel OFDMA. Proceedings of Proc. IEEE ICC, 2007. 5439 - 5444.
[66] Standard I . Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, Aug., 1999.
[67] Standard I . IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface, 2004.
[68] Gummalla A C V, Limb J O. Wireless Medium Access Control Protocols. IEEE Commun.Surveys, 2000. 2-15.
[69] Bertsekas D, Gallager R. Data Networks. 2nd ed., Prentice Hall, 1992.
[
70] Bahl P, Chandra R, Dunagan J. SSCH: Slotted Seeded Channel Hopping for Capacity Improvement in IEEE 802.11 Ad-Hoc Wireless Networks. Proceedings of Proc. of Mobi-com'04,2004. 216-230.
[71] Mo J, So H, Walrand J. Comparison of Multi-Channel MAC Protocols. Proceedings of Proc. ACM/IEEE MSWiM'05,2005. 209-218.
[72] Nee R V, Prasad R. OFDM for Wireless Multimedia Communications. Artech House, 2000.
[73] Hanzo L, Munster M, Choi B J, et al. OFDM and MC-CDMA for Broadband Multi-user Communications, WLANs, and Broadcasting. Wiley, 2003.
[74] Standard E D. Digital Audio Broadcasting (DAB), May, 1997.
[75] Standard E D. Digital Video Broadcasting (DVB): Framing structure, channel coding and modulation for digital terrestrial television, Aug., 1997.
[76] Wyglinski A M. Effects of Bit Allocation on Non-Contiguous Multicarrier-Based Cognitive Radio Transceivers. Proceedings of Proc. IEEE VTC, 2006. 1-5.
[77] Poston J D, Home W D. Discontiguos OFDM considerations for dynamic spectrum access in idle TV channels. Proceedings of Proc. IEEE DySPAN, 2005. 607-610.
[78] 3GPP2. Physical Layer for Ultra Mobile Broadband UMB Air Interface Specification, Apr.,2007.
[79] Letaief K B, Zhang Y J. Dynamic multiuser resource allocation and adaptation for wireless systems. IEEE Wireless Commun. Mag., 2006, 13:38-47.
[80] Kivanc D, Li G, Liu H. Computationally Efficient Bandwidth Allocation and Power Control for OFDMA. IEEE Trans. on Wireless Commun., 2003,2(6):1150-1158.
[81] Bohge M, Gross J, Wolisz A, et al. Dynamic Resource Allocation in OFDM Systems: An Overview of Cross-Layer Optimization Principles and Techniques. IEEE Network, 2007.53-59.
[82] Qiu X, Chawla K. On the performance of adaptive modulation in cellular systems. IEEE Trans. Commn., 1999, 47(6):884-895.
[83] Alouini M S, Goldsmith A J. Adaptive Modulation over Nakagami Fading Channels. Wire- less Personal Communications, 2000, 13(1-2):119-143.
[84] Agresti A. Categorical Data Analysis. 2nd ed., Wiley, 2002.
[85] Meyer C D. Matrix Analysis and Applied Linear Algebra. SIAM, 2000.
[86] Niyato D, Hossain E. Hierarchical Spectrum Sharing in Cognitive Radio: A Microeconomic Approach. Proceedings of Proc. IEEE WCNC, 2007. 3822-3826.
[87] Goldsmith A J, Chua S G. Variable Rate Power MQAM for Fading Channels. IEEE Trans.onCommun, 1997, 45(10):1218-1230.
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