认知无线电网络动态资源优化理论研究
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摘要
随着人们生活水平的逐步提高,对无线通信服务的要求也从单一的语音通信发展为更为丰富的多媒体数据通信,这就使得对于无线频谱资源的需求越来越大。但现有的频谱管理政策是基于固定分配、授权使用的方式,由政府的无线电管理部门对频谱资源统一分配,以美国的3kHz～300GHz频段为例,该频段已经被划分授权给不同的服务业务,可供分配使用的空白频段所剩无几。同时,根据美国联邦通信委员会(Federal Communication Committee, FCC)的报告,对频谱资源的利用随时间和地域的不同存在很大的差异性,很多频段的频谱利用率不高。为了解决可用频谱资源匮乏与频谱利用率不高的矛盾,“认知无线电”(Cognitive Radio, CR)技术凭借其“灵敏的频谱感知、智能的频谱接入”等特性受到人们的广泛关注,成为国内外研究的热点。
在本论文中,我们主要研究了认知无线电网络中的无线资源分配问题。根据网络系统的不同结构,我们采用不同的频谱共享模式,分别研究了认知蜂窝移动网络、认知多载波网络和认知传感器网络中的功率控制、载波分配和时隙调度等资源优化问题。通过数学建模,我们将所研究的问题建模为凸优化问题,利用凸优化理论与对偶分解理论对问题进行求解,以求得对无线资源分配的最优算法。本论文主要有以下几项贡献和创新点：
1.研究了认知蜂窝移动网络中多用户功率控制问题。认知用户网络采用中心式蜂窝移动网络结构,认知用户以Underlay频谱共享模式与主要用户共享授权频段,并以码分多址(Code Division Multiple Access, CDMA)方式接入到认知用户网络。我们以最大化认知用户的总信道容量为研究目标,同时满足主要用户对干扰功率的要求。由于初始问题的建模是一个非线性、非凸优化问题,不能保证最终的优化结果为全局最优,我们利用几何规划(Geometric Programming, GP)理论将这一非线性、非凸优化问题转化为非线性、凸优化的问题。同时由于干扰信道中认知用户之间效用函数的优化变量相互耦合,这样不便于进行分布式求解,我们利用耦合分解方法和对偶分解理论将系统的总优化问题分解为各个认知用户的子优化问题,并保证了最终解的全局最优性。
2.研究了在认知多载波网络中认知用户的载波与功率分配算法。我们根据Overlay与Underlay两种频谱共享模式的特点,提出了一种混合机会频谱接入(HybridOpportunistic Spectrum Access, H-OSA)算法,即认知用户不但可以接入主要用户的“空闲”信道,还可以在保证主要用户干扰约束的条件下接入“占用”信道,这样既可以有效地使用频谱空洞资源又可以最大限度地利用主要用户的容忍门限,从而最大限度地提高频谱利用率。我们首先在单认知用户系统中分析了混合机会频谱接入算法,通过对偶分解理论将所建模问题分解为多个子问题,从而得出认知用户在各个载波信道上的功率最优值；接着我们分别在认知多载波网络下行链路与上行链路环境中分析了混合机会频谱接入算法,推导出在该算法下最优的多用户载波分配与功率控制策略；最后,当考虑认知用户对主要用户的干扰信道状态信息为非理想信道状态信息时,我们分析了混合机会频谱接入算法的性能变化。
3.研究了在认知传感器网络中基于能量有效性的时隙调度与功率控制算法。认知用户网络采用Ad Hoc网络系统结构,主要用户与认知用户成对存在且在网络中随机分布。研究目标为在保证主要用户与认知用户的QOS条件下,最小化认知用户的传输功率。通过“频谱租借”方式,主要用户在满足自己最小传输速率的前提下将剩余传输时隙分配给认知用户,而认知用户采用时分多址(Time Division Multiple Access, TDMA)方式接入传输时隙与主要用户共享授权频段。通过采用中继节点的协作传输,认知传感器节点可以利用信道状态更好的传输路径传输信息以提高用户的信道容量,从而使认知用户在保证最小平均传输速率的同时有效地减少了所需的传输功率,提高了系统的能量有效性。
As the improvement of people's life, the requirements of wireless communication service are changed from the voice communication to the multi-media data communi-cation. So more and more spectrum resource are needed to support the service require-ments. But the ongoing spectrum management policies are based on fixed allocation and licensed usage. The spectrum allocation is managed by the radio management committee of the government in each country. For example, the spectrum band be-tween 3kHz and 300GHz has been licensed to the different service applications, and the available idle bands barely remain. Meanwhile, based on the report of Federal Communication Committee (FCC), the spectrum utility varies in different time and geographic location. Most of the spectrum has a very low utility. In order to solve the problem of the spectrum scarcity and the low spectrum utility, " Cognitive Radio (CR)" has attracted attention as the research topic for its properties, such as sensitive spectrum sensing and intelligent spectrum access.
In this thesis, we mainly studied the wireless resource allocation problem in the cognitive radio networks. Based on the different network structures, using different spectrum sharing models, we studied the power control, carrier allocation and times-lot scheduling problem in cognitive cellular networks, cognitive multi-carrier networks and cognitive sensor networks, respectively. Through the mathematic formulation, we formulate the research problem into the convex optimization problem. So the problem can be solved using convex optimization theory and dual decomposition theory and obtain the optimal resource allocation algorithm. Main contributions of the thesis are as follows:
1. We studied the multi-user power control problem in the cognitive cellular networks. The cognitive user networks apply the centralized cellular network structure. The cognitive users share the licensed spectrum with the primary user using the Un-derlay spectrum sharing model and access the cognitive user networks in Code Division Multiple Access (CDMA). The research objective is to maximize the total capacity of the cognitive users, satisfying the constraint of the interference power to the primary user. Due to that the proposed problem formulation is a nonlin-ear and nonconvex problem which couldn't guarantee obtaining the global optimal solution, we introduce the Geometric Programming (GP) theory to transform the nonlinear and nonconvex problem into nonlinear and convex problem. As the vari-ables in the utility functions between the cognitive users arc coupled with each other which couldn't lead to a distributed solution, we introduce the couple dc-composition method and dual decomposition theory to separate the optimization problem for the system into optimization sub-problem for the cognitive users and obtain the final global optimal solutions.
2. We studied the carrier and power allocation for cognitive users in cognitive multi-carrier networks. Based on the characteristic of Overlay and Underlay spectrum sharing model, we proposed a Hybrid Opportunistic Spectrum Access (H-OSA) Algorithm. In this algorithm, the cognitive user could access not only the idle channels but also the channels occupied by the primary user, guaranteeing the in-terfcrence constraints for primary user. So it could both utilize the spectrum holes more efficiently and take advantage of the primary user's tolerance threshold. This could improve the spectrum utility effectively. We first analysed the H-OSA algo-rithm in the singlc-cognitivc-user system. We obtain the optimal power solution in each carrier through the dual decomposition theory which separate the pro-posed problem into several sub-problem. Then, we studied the H-OSA algorithm in the cognitive multi-carrier networks, considering downlink and uplink scenar-ios, respectively. We deduced the optimal carrier and power allocation strategies for both scenarios. At last, considering that the information of the interference channel to primary user is imperfect channel state information, we analysed the performance of the H-OSA algorithm.
3. We studied the timeslot scheduling and power control based on energy efficiency in cognitive sensor networks. The cognitive user networks apply the Ad Hoc net- work structure. The primary users and cognitive users exist in pairs. The research objective is to minimize the transmission power of cognitive user, guaranteeing the Quality of Service (QoS) of the primary user and cognitive user. Using the "spectrum leasing" model, the primary users first achieve their minimal required transmission rates, and then, lease the residual transmission timeslots to the cogni-tive users. The cognitive users access the transmission timeslots in Time Division Multiple Access (TDMA) model. Through the cooperative transmission by relay nodes, the cognitive sensors could take advantage of the transmission path which has a better channel state to improve the channel capacity. So it could effectively save the transmission power and satisfying the minimal required transmission rates of cognitive users.
在本论文中,我们主要研究了认知无线电网络中的无线资源分配问题。根据网络系统的不同结构,我们采用不同的频谱共享模式,分别研究了认知蜂窝移动网络、认知多载波网络和认知传感器网络中的功率控制、载波分配和时隙调度等资源优化问题。通过数学建模,我们将所研究的问题建模为凸优化问题,利用凸优化理论与对偶分解理论对问题进行求解,以求得对无线资源分配的最优算法。本论文主要有以下几项贡献和创新点：
1.研究了认知蜂窝移动网络中多用户功率控制问题。认知用户网络采用中心式蜂窝移动网络结构,认知用户以Underlay频谱共享模式与主要用户共享授权频段,并以码分多址(Code Division Multiple Access, CDMA)方式接入到认知用户网络。我们以最大化认知用户的总信道容量为研究目标,同时满足主要用户对干扰功率的要求。由于初始问题的建模是一个非线性、非凸优化问题,不能保证最终的优化结果为全局最优,我们利用几何规划(Geometric Programming, GP)理论将这一非线性、非凸优化问题转化为非线性、凸优化的问题。同时由于干扰信道中认知用户之间效用函数的优化变量相互耦合,这样不便于进行分布式求解,我们利用耦合分解方法和对偶分解理论将系统的总优化问题分解为各个认知用户的子优化问题,并保证了最终解的全局最优性。
2.研究了在认知多载波网络中认知用户的载波与功率分配算法。我们根据Overlay与Underlay两种频谱共享模式的特点,提出了一种混合机会频谱接入(HybridOpportunistic Spectrum Access, H-OSA)算法,即认知用户不但可以接入主要用户的“空闲”信道,还可以在保证主要用户干扰约束的条件下接入“占用”信道,这样既可以有效地使用频谱空洞资源又可以最大限度地利用主要用户的容忍门限,从而最大限度地提高频谱利用率。我们首先在单认知用户系统中分析了混合机会频谱接入算法,通过对偶分解理论将所建模问题分解为多个子问题,从而得出认知用户在各个载波信道上的功率最优值；接着我们分别在认知多载波网络下行链路与上行链路环境中分析了混合机会频谱接入算法,推导出在该算法下最优的多用户载波分配与功率控制策略；最后,当考虑认知用户对主要用户的干扰信道状态信息为非理想信道状态信息时,我们分析了混合机会频谱接入算法的性能变化。
3.研究了在认知传感器网络中基于能量有效性的时隙调度与功率控制算法。认知用户网络采用Ad Hoc网络系统结构,主要用户与认知用户成对存在且在网络中随机分布。研究目标为在保证主要用户与认知用户的QOS条件下,最小化认知用户的传输功率。通过“频谱租借”方式,主要用户在满足自己最小传输速率的前提下将剩余传输时隙分配给认知用户,而认知用户采用时分多址(Time Division Multiple Access, TDMA)方式接入传输时隙与主要用户共享授权频段。通过采用中继节点的协作传输,认知传感器节点可以利用信道状态更好的传输路径传输信息以提高用户的信道容量,从而使认知用户在保证最小平均传输速率的同时有效地减少了所需的传输功率,提高了系统的能量有效性。
As the improvement of people's life, the requirements of wireless communication service are changed from the voice communication to the multi-media data communi-cation. So more and more spectrum resource are needed to support the service require-ments. But the ongoing spectrum management policies are based on fixed allocation and licensed usage. The spectrum allocation is managed by the radio management committee of the government in each country. For example, the spectrum band be-tween 3kHz and 300GHz has been licensed to the different service applications, and the available idle bands barely remain. Meanwhile, based on the report of Federal Communication Committee (FCC), the spectrum utility varies in different time and geographic location. Most of the spectrum has a very low utility. In order to solve the problem of the spectrum scarcity and the low spectrum utility, " Cognitive Radio (CR)" has attracted attention as the research topic for its properties, such as sensitive spectrum sensing and intelligent spectrum access.
In this thesis, we mainly studied the wireless resource allocation problem in the cognitive radio networks. Based on the different network structures, using different spectrum sharing models, we studied the power control, carrier allocation and times-lot scheduling problem in cognitive cellular networks, cognitive multi-carrier networks and cognitive sensor networks, respectively. Through the mathematic formulation, we formulate the research problem into the convex optimization problem. So the problem can be solved using convex optimization theory and dual decomposition theory and obtain the optimal resource allocation algorithm. Main contributions of the thesis are as follows:
1. We studied the multi-user power control problem in the cognitive cellular networks. The cognitive user networks apply the centralized cellular network structure. The cognitive users share the licensed spectrum with the primary user using the Un-derlay spectrum sharing model and access the cognitive user networks in Code Division Multiple Access (CDMA). The research objective is to maximize the total capacity of the cognitive users, satisfying the constraint of the interference power to the primary user. Due to that the proposed problem formulation is a nonlin-ear and nonconvex problem which couldn't guarantee obtaining the global optimal solution, we introduce the Geometric Programming (GP) theory to transform the nonlinear and nonconvex problem into nonlinear and convex problem. As the vari-ables in the utility functions between the cognitive users arc coupled with each other which couldn't lead to a distributed solution, we introduce the couple dc-composition method and dual decomposition theory to separate the optimization problem for the system into optimization sub-problem for the cognitive users and obtain the final global optimal solutions.
2. We studied the carrier and power allocation for cognitive users in cognitive multi-carrier networks. Based on the characteristic of Overlay and Underlay spectrum sharing model, we proposed a Hybrid Opportunistic Spectrum Access (H-OSA) Algorithm. In this algorithm, the cognitive user could access not only the idle channels but also the channels occupied by the primary user, guaranteeing the in-terfcrence constraints for primary user. So it could both utilize the spectrum holes more efficiently and take advantage of the primary user's tolerance threshold. This could improve the spectrum utility effectively. We first analysed the H-OSA algo-rithm in the singlc-cognitivc-user system. We obtain the optimal power solution in each carrier through the dual decomposition theory which separate the pro-posed problem into several sub-problem. Then, we studied the H-OSA algorithm in the cognitive multi-carrier networks, considering downlink and uplink scenar-ios, respectively. We deduced the optimal carrier and power allocation strategies for both scenarios. At last, considering that the information of the interference channel to primary user is imperfect channel state information, we analysed the performance of the H-OSA algorithm.
3. We studied the timeslot scheduling and power control based on energy efficiency in cognitive sensor networks. The cognitive user networks apply the Ad Hoc net- work structure. The primary users and cognitive users exist in pairs. The research objective is to minimize the transmission power of cognitive user, guaranteeing the Quality of Service (QoS) of the primary user and cognitive user. Using the "spectrum leasing" model, the primary users first achieve their minimal required transmission rates, and then, lease the residual transmission timeslots to the cogni-tive users. The cognitive users access the transmission timeslots in Time Division Multiple Access (TDMA) model. Through the cooperative transmission by relay nodes, the cognitive sensors could take advantage of the transmission path which has a better channel state to improve the channel capacity. So it could effectively save the transmission power and satisfying the minimal required transmission rates of cognitive users.
引文
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