认知无线网络的网络架构及关键技术研究
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摘要
无线网络的不断发展,在用户需求得到充分满足的同时,却仍然面临诸多问题。一方面,为了满足用户不断增长的业务需求,各种不同制式无线网络相继出现,导致异构网络的互联互通问题日益严重。另一方面,无线频谱资源非常宝贵,但传统频谱管理方式都是静态的。根据权威机构的调查研究发现,目前出现的频谱资源短缺问题,不仅是因为频谱资源的物理受限,另一个主要原因来自于频谱资源管理策略欠佳。
正是在这样的背景之下,认知无线电技术应运而生。认知无线电以其灵活、自主和适变的特性可以成功解决异构网络融合问题,有效提升网络的频谱使用效率,已经成为近期研究的热点。而基于认知无线电技术的认知无线网络更是得到了学术界和工业界的广泛关注。本文针对认知无线网络发展所面临的新需求和新挑战,重点针对认知无线网络的网络架构、频谱共享方案以及能量有效性传输方案等方面展开了深入研究,并在相关领域取得了理论和技术创新成果。通过本文的研究,可以解决目前无线网络的异构融合问题,改善频谱和能量的使用效率。归纳来说,本文研究的内容主要包括以下三个部分：
第一部分提出了一种系统化、功能全、可以实现异构网络融合的智能认知无线网络架构。所提网络架构从异构融合和智能性两个方面出发,通过对现有关于认知无线网络架构研究的优缺点进行分析,采用引入新功能模块的方式,如认知信息管理模块、智能管理模块、网络重构模块等,进一步提升网络能力。其中认知信息管理模块可以提供更为完备的认知信息获取、表征、传递以及存储功能,提升了网络的认知能力。智能管理模块可以为其它功能模块提供学习推理功能,提升了网络的自主能力,相当于网络的智能性。网络重构模块可以根据网络状况,适配网络参数,提升了网络的适变能力,为用户提供不同制式网络的无缝接入与切换,从而实现异构网络融合。此外,本文还详细描述了网络架构中各模块的功能及相互之间的交互关系。
第二部分利用图论对认知OFDM网络的频谱共享方案(主要包括信道分配和功率分配)进行了研究。利用图论研究频谱共享方案的关键在于图形建模,与传统方案不同的是,本文将次级链路建模为图的顶点,边表示两个次级链路是否相互干扰,并采用权重进一步描述不同链路之间的干扰程度。为了避免精确的干扰计算,本文采用用户之间的相对地理位置确定不同链路之间的干扰等级。然后基于所构造的干扰图,提出了一种低复杂度的两阶段启发式算法,并分析了算法的最优性以及复杂度,结果表明算法可以获得与最优方案非常接近的性能,与原始的NP-Hard问题相比,复杂度仅为O)(N2/2+N/2+M),其中N表示次级链路数,M表示可用信道数。此外,还对所提方案进行了扩展,即在设置加权干扰图的权重时,充分考虑主用户位置的影响。通过仿真可以发现,所提算法可以大大提升网络的频谱效率,而且加权干扰图的权重会对网络性能产生很大影响,考虑主用户位置的方案可以进一步提升网络性能。
第三部分研究了认知无线网络中混合频谱共享下的能量有效性传输方案。本文采用比特每焦耳来描述网络的能量有效性,即每焦耳能量所能传输的数据比特数,并与所定义的混合频谱共享模型相结合,给出了相应的优化问题模型。由于原始优化问题过于复杂,本文分别从功率分配、感知时长选择两个方面单独分析最优方案的存在性和唯一性,提出了一种低复杂度的联合迭代算法以逼近最优方案,原始问题的最优方案只能通过穷搜方式获得,而所提算法的复杂度仅为O(Ns*Nt),其中Ns为最优感知时长选择方案的迭代次数,Nt为最优功率分配方案的迭代次数,Ns和Nt通常都很小。此外,还分析了检测门限选择对设计能量有效性传输方案的影响,理论分析结果表明合理选择检测门限可以降低优化的复杂度。仿真结果验证了能量有效性传输在提高能量利用效率方面的优势,给出了混和频谱共享方式以及低复杂度联合迭代算法的性能增益。
With the rapidly development of wireless networks, the users' requirements are fully satisfied. Meanwhile, it also introduces many challenges. On one hand, in order to meet the user's increasing service requirements, various wireless networks with different operation modes have emerged. Hence, the interconnection between heterogeneous networks becomes very important. On the other hand, spectrum resources are very precious and the traditional spectrum management methods are static. Furthermore, according to the research reports of authoritative organizations, e.g. FCC, the spectrum resources shortage problem is not just because the physical limit of spectrum resources, but the poor spectrum management schemes.
Under this background, the CR (Cognitive Radio) technique is born. With the characteristics of flexibility, autonomy and adaptation, CR can solve the heterogeneous networks interconnection problem and improve the network spectrum efficiency, so study on CR has become one of the popular topics in wireless communications. CRN (Cognitive Radio Networks), which based on CR, have received a lot of attention recently. In order to solve the challenges of CRN, we focus on the research of novel network architecture, spectrum sharing schemes and energy-efficient transmission schemes. We also obtain some theory and technique innovation achievements. By studying the contents of this thesis, we can provide solution to heterogeneity interconnection and improve the spectrum and energy efficiency. In all, the key contributions of this thesis include three parts:
Firstly, we proposed a novel network architecture for CRN, which enjoys systematization, full functions, heterogeneous interconnection and intellectuality. From two aspects of heterogeneous interconnection and intellectuality, the proposed network architecture improves the network capability by introducing some new functional module, e.g. cognitive information management module, smart management module, network reconfiguration module and so on. The cognitive information management module can provide complete functions of cognitive information acquisition, representation, transmission and storage, so the network's cognitive capability is improved. The smart management module can provide learning and reasoning functions to other modules, which will improve network's autonomic capability. The network reconfiguration module can change the operation parameters according to the network states and improve the network's adaptive capability. Then it can provide seamless access and handover among different network modes to users, which means heterogeneous network interconnection. In addition, the functions of each module and the interactions between modules in the proposed network architecture are described in detail.
Secondly, spectrum sharing scheme in cognitive OFDM networks, which includes channel allocation and power allocation, is investigated by using graph theory. The key point of using graph theory to solve spectrum sharing problem is graph construction. Different from the traditional scheme, the secondary links is modeled as the vertex of graph and the edge represent whether the two links is interfere with each other. Furthermore, we adopt weight to describe the interference strength. To avoid the accurate computation of interference, we adopt the relative geographical positions between different links to describe the interference level. Then the weighted interference graph is constructed. Based on the constructed graph, we proposed a low-complexity two-stage heuristic algorithm and analyzed the optimality and complexity of the proposed algorithm. The analysis result indicates that the proposed algorithm can obtain approximate performance with the optimal scheme, but the computational complexity decreased toO(N2/2+N12+M), where N represents the number of secondary link, M represents the number of channel. In addition, we further extend the scheme by considering primary users'location when setting the weight of weighted interference graph. From the simulation results, the proposed scheme can improve the network spectrum efficiency considerably and the weight selection has an enormous effect on the network performance. The primary users' locations are considered when setting weight can further improve the network performance.
Thirdly, the energy-efficient transmission under hybrid spectrum sharing method in CRN is investigated. Here we adopt the bits/joule to describe energy-efficiency, which represents the transmitted bits per joule consumed. Then the optimization model of energy-efficient transmission under hybrid spectrum sharing is given. Since the original optimization problem is too complex, we analyze the existence and uniqueness of optimal scheme from two aspects independently. One for power allocation, the other is sensing duration selection. Then a low-complexity iterative algorithm is proposed to approximate the optimal scheme. The complexity of proposed algorithm is O(Ns*Nt), where Ns and Nt are the number of iterations for find the optimal sensing duration and transmission power respectively. In addition, we also analyze the effect of detection threshold selection on energy-efficient transmission, the theoretical analysis results indicate that the complexity of optimization problem can be reduced by proper selecting the detection threshold. Simulation results verify that energy-efficient transmission can improve the energy efficiency greatly, and the performance gains of hybrid spectrum sharing and low-complexity iterative algorithm are given.
正是在这样的背景之下,认知无线电技术应运而生。认知无线电以其灵活、自主和适变的特性可以成功解决异构网络融合问题,有效提升网络的频谱使用效率,已经成为近期研究的热点。而基于认知无线电技术的认知无线网络更是得到了学术界和工业界的广泛关注。本文针对认知无线网络发展所面临的新需求和新挑战,重点针对认知无线网络的网络架构、频谱共享方案以及能量有效性传输方案等方面展开了深入研究,并在相关领域取得了理论和技术创新成果。通过本文的研究,可以解决目前无线网络的异构融合问题,改善频谱和能量的使用效率。归纳来说,本文研究的内容主要包括以下三个部分：
第一部分提出了一种系统化、功能全、可以实现异构网络融合的智能认知无线网络架构。所提网络架构从异构融合和智能性两个方面出发,通过对现有关于认知无线网络架构研究的优缺点进行分析,采用引入新功能模块的方式,如认知信息管理模块、智能管理模块、网络重构模块等,进一步提升网络能力。其中认知信息管理模块可以提供更为完备的认知信息获取、表征、传递以及存储功能,提升了网络的认知能力。智能管理模块可以为其它功能模块提供学习推理功能,提升了网络的自主能力,相当于网络的智能性。网络重构模块可以根据网络状况,适配网络参数,提升了网络的适变能力,为用户提供不同制式网络的无缝接入与切换,从而实现异构网络融合。此外,本文还详细描述了网络架构中各模块的功能及相互之间的交互关系。
第二部分利用图论对认知OFDM网络的频谱共享方案(主要包括信道分配和功率分配)进行了研究。利用图论研究频谱共享方案的关键在于图形建模,与传统方案不同的是,本文将次级链路建模为图的顶点,边表示两个次级链路是否相互干扰,并采用权重进一步描述不同链路之间的干扰程度。为了避免精确的干扰计算,本文采用用户之间的相对地理位置确定不同链路之间的干扰等级。然后基于所构造的干扰图,提出了一种低复杂度的两阶段启发式算法,并分析了算法的最优性以及复杂度,结果表明算法可以获得与最优方案非常接近的性能,与原始的NP-Hard问题相比,复杂度仅为O)(N2/2+N/2+M),其中N表示次级链路数,M表示可用信道数。此外,还对所提方案进行了扩展,即在设置加权干扰图的权重时,充分考虑主用户位置的影响。通过仿真可以发现,所提算法可以大大提升网络的频谱效率,而且加权干扰图的权重会对网络性能产生很大影响,考虑主用户位置的方案可以进一步提升网络性能。
第三部分研究了认知无线网络中混合频谱共享下的能量有效性传输方案。本文采用比特每焦耳来描述网络的能量有效性,即每焦耳能量所能传输的数据比特数,并与所定义的混合频谱共享模型相结合,给出了相应的优化问题模型。由于原始优化问题过于复杂,本文分别从功率分配、感知时长选择两个方面单独分析最优方案的存在性和唯一性,提出了一种低复杂度的联合迭代算法以逼近最优方案,原始问题的最优方案只能通过穷搜方式获得,而所提算法的复杂度仅为O(Ns*Nt),其中Ns为最优感知时长选择方案的迭代次数,Nt为最优功率分配方案的迭代次数,Ns和Nt通常都很小。此外,还分析了检测门限选择对设计能量有效性传输方案的影响,理论分析结果表明合理选择检测门限可以降低优化的复杂度。仿真结果验证了能量有效性传输在提高能量利用效率方面的优势,给出了混和频谱共享方式以及低复杂度联合迭代算法的性能增益。
With the rapidly development of wireless networks, the users' requirements are fully satisfied. Meanwhile, it also introduces many challenges. On one hand, in order to meet the user's increasing service requirements, various wireless networks with different operation modes have emerged. Hence, the interconnection between heterogeneous networks becomes very important. On the other hand, spectrum resources are very precious and the traditional spectrum management methods are static. Furthermore, according to the research reports of authoritative organizations, e.g. FCC, the spectrum resources shortage problem is not just because the physical limit of spectrum resources, but the poor spectrum management schemes.
Under this background, the CR (Cognitive Radio) technique is born. With the characteristics of flexibility, autonomy and adaptation, CR can solve the heterogeneous networks interconnection problem and improve the network spectrum efficiency, so study on CR has become one of the popular topics in wireless communications. CRN (Cognitive Radio Networks), which based on CR, have received a lot of attention recently. In order to solve the challenges of CRN, we focus on the research of novel network architecture, spectrum sharing schemes and energy-efficient transmission schemes. We also obtain some theory and technique innovation achievements. By studying the contents of this thesis, we can provide solution to heterogeneity interconnection and improve the spectrum and energy efficiency. In all, the key contributions of this thesis include three parts:
Firstly, we proposed a novel network architecture for CRN, which enjoys systematization, full functions, heterogeneous interconnection and intellectuality. From two aspects of heterogeneous interconnection and intellectuality, the proposed network architecture improves the network capability by introducing some new functional module, e.g. cognitive information management module, smart management module, network reconfiguration module and so on. The cognitive information management module can provide complete functions of cognitive information acquisition, representation, transmission and storage, so the network's cognitive capability is improved. The smart management module can provide learning and reasoning functions to other modules, which will improve network's autonomic capability. The network reconfiguration module can change the operation parameters according to the network states and improve the network's adaptive capability. Then it can provide seamless access and handover among different network modes to users, which means heterogeneous network interconnection. In addition, the functions of each module and the interactions between modules in the proposed network architecture are described in detail.
Secondly, spectrum sharing scheme in cognitive OFDM networks, which includes channel allocation and power allocation, is investigated by using graph theory. The key point of using graph theory to solve spectrum sharing problem is graph construction. Different from the traditional scheme, the secondary links is modeled as the vertex of graph and the edge represent whether the two links is interfere with each other. Furthermore, we adopt weight to describe the interference strength. To avoid the accurate computation of interference, we adopt the relative geographical positions between different links to describe the interference level. Then the weighted interference graph is constructed. Based on the constructed graph, we proposed a low-complexity two-stage heuristic algorithm and analyzed the optimality and complexity of the proposed algorithm. The analysis result indicates that the proposed algorithm can obtain approximate performance with the optimal scheme, but the computational complexity decreased toO(N2/2+N12+M), where N represents the number of secondary link, M represents the number of channel. In addition, we further extend the scheme by considering primary users'location when setting the weight of weighted interference graph. From the simulation results, the proposed scheme can improve the network spectrum efficiency considerably and the weight selection has an enormous effect on the network performance. The primary users' locations are considered when setting weight can further improve the network performance.
Thirdly, the energy-efficient transmission under hybrid spectrum sharing method in CRN is investigated. Here we adopt the bits/joule to describe energy-efficiency, which represents the transmitted bits per joule consumed. Then the optimization model of energy-efficient transmission under hybrid spectrum sharing is given. Since the original optimization problem is too complex, we analyze the existence and uniqueness of optimal scheme from two aspects independently. One for power allocation, the other is sensing duration selection. Then a low-complexity iterative algorithm is proposed to approximate the optimal scheme. The complexity of proposed algorithm is O(Ns*Nt), where Ns and Nt are the number of iterations for find the optimal sensing duration and transmission power respectively. In addition, we also analyze the effect of detection threshold selection on energy-efficient transmission, the theoretical analysis results indicate that the complexity of optimization problem can be reduced by proper selecting the detection threshold. Simulation results verify that energy-efficient transmission can improve the energy efficiency greatly, and the performance gains of hybrid spectrum sharing and low-complexity iterative algorithm are given.
引文
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