认知无线电频谱资源分配与共享技术研究
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摘要
随着无线通信技术的发展，人们对无线频谱资源需求的日益增长，使得有限的无线频谱资源变得愈加紧张。尽管采用新的无线通信理论及技术，如链路自适应技术、多天线技术等，可以在一定程度上提高频谱效率，但远不能满足人们不断增长的频谱资源的需求。然而调查研究表明，现阶段固定式的频谱资源划分方案并未使得有限的频谱资源被有效地利用。为此，认知无线电技术(Cognitive Radio，CR)作为一种解决现阶段频谱资源紧缺与频谱资源利用率低之间矛盾的有效技术手段受到广泛关注。
本文主要研究认知无线电网络频谱资源分配与共享技术，其主要内容如下：
首先，分析了认知无线电通信系统的频谱共享问题，利用图着色模型讨论了填充式共享的认知无线电网络中的频谱分配算法。针对网络中认知节点移动对应重新计算认知用户的频谱分配矩阵的问题，提出一种改进的基于图着色模型的频谱分配算法，该算法建立在已获得频谱分配的基础上，通过引起干扰冲突的认知节点退出相应授权频带的占用及寻找空闲授权频带，降低了节点移动对频谱分配的影响。同时，分析了智能算法在认知无线电网络频谱分配中的应用，针对传统遗传算法的频谱分配模型求解过程中约束条件的处理进行深入分析，提出了一种基于约束模板的认知无线电网络遗传算法频谱分配模型。该算法根据认知用户间干扰关系，以系统效用最大化为目标产生无干扰的约束模板，再以约束模板的组合构成染色体，通过遗传算法进化得到使系统效用最大的约束模板组合，并对认知无线电网络频谱分配算法的性能进行比较与分析。
其次，分析了基于覆盖式共享的认知无线电网络中的功率分配问题，提出一种基于博弈论认知无线电网络分步功率分配算法，将认知无线电网络的功率分配问题分解为目标信干比的求解与功率分配方案的求解，该算法可以在获得相似系统吞吐量的条件下降低认知用户数据传输的功率消耗水平。同时，为了更好地描述认知用户通信对授权用户通信质量的影响，分析了基于授权用户反馈控制信息的频谱共享模型，提出一种基于授权用户反馈控制信息的改进频谱共享算法。该算法结合最优化理论与反馈控制信息，相比传统算法可快速迭代求解认知用户的功率分配策略，更好地保证授权用户的中断概率性能。
再次，分析了基于频谱租赁机制的认知无线电网络的频谱共享问题，主要探讨了基于竞价博弈的及拍卖机制的频谱资源共享问题。分析了授权用户可提供频谱资源受限条件下的竞价频谱共享问题，利用拉格朗日乘子法建立了满足授权用户频谱资源约束条件的优化模型，并利用结合最速梯度迭代算法提出了一种快速求解授权用户共享频谱价格策略的方法，可避免传统线性迭代算法中预先根据用户通信参数确定调整因子的环节，快速得到满足约束条件的共享频谱价格。同时，结合竞价博弈与拍卖机制提出一种考虑认知用户需求的分级频谱租赁模型，可用于分析具有多个授权用户与认知用户的网络模型中的频谱租赁问题。
最后，分析了认知无线电网络中的频谱接入技术，讨论了认知无线电网络中的频谱感知收益与频谱感知开销问题，提出了一种基于授权频带空闲概率模型的认知用户频谱接入算法，以不同授权频带的空闲概率为基础，合理选择参与协作感知的认知用户，优先对概率模型中空闲概率高的授权频带进行感知，考虑了认知用户的传输特性，可降低频谱感知开销获得更高的频谱感知收益，更高效地寻找认知无线电网络中的频谱接入机会。
With the development of wireless communication technology, the demand for wirelessspectrum has been growing tremendously, and leads to increasing scarcity in wirelessspectrum resource. Although the advanced wireless communication techniques are applied,such as the link adaptation and multi-antenna technology, which can improve the spectrumefficiency to some extent, the increasing demand of wireless spectrum resource requirementsstill can’t be satisfied. However, according to the research results, the spectrum resource in theexisting static spectrum sharing is not fully utilized. The cognitive radio technology hasattracted tremendous attention, and becomes an effective technique to alleviate thecontradiction between the scarcity and low efficiency of the existing static spectrum sharingscheme.
This paper focuses on spectrum allocation and sharing of the cognitive radio networks, itmainly includes:
Firstly, the spectrum sharing problem of the cognitive radio communication system isanalyzed. The list coloring algorithm is applied to discuss the spectrum allocation problem inthe overlay cognitive radio networks. In order to avoid the recalculation of the spectrumallocation with the mobility of the cognitive users, an improved list coloring based spectrumallocation algorithm is proposed. On the basis of previous spectrum allocation, it reduces theimpact of secondary users’ mobility to the spectrum allocation by releasing correspondingconflicted channels and searching for available channels. In addition, the spectrum allocationbased on intelligent algorithm is further analyzed, and the constraint templates based geneticspectrum assignment model for cognitive radio networks is proposed. The noninterferenceconstraint templates are calculated aiming at maximizing the system utility, and thechromosome in the proposed algorithm is composed of the indexes of the constraint templates.Thus the combination of the constraint templates with high fitness is obtained through theevolution of genetic operators, and the corresponding feasible spectrum assignment strategywithout interference can be achieved. The performance of the spectrum assignmentalgorithms for cognitive radio networks are compared and evaluated by simulation results.
Secondly, the power distribution in the cognitive radio networks based on underlay spectrum sharing model is analyzed, and a step power distribution algorithm based on gametheory is proposed. The power distribution of the cognitive radio networks can be divided into the calculation of target signal to noise ratio and the calculation of power distribution. Itcan achieve the similar throughput performance with lower consumed power levels.Meanwhile, in order to control the impact of secondary transmission on the communicationquality of licensed users, the spectrum sharing model based on the feedback controlinformation is analyzed, and an improve power distribution algorithm based on feedbackcontrol information from the primary user is proposed. It combines the optimization theoryand feedback control information, and can obtain the power distribution strategies with lessiteration than the conventional algorithm. It can also get better outage probabilityperformance of the primary users.
Thirdly, the spectrum sharing problem based on spectrum leasing mechanism incognitive radio networks is analyzed, the spectrum sharing models based on competitive pricegame and auction mechanism are discussed. The spectrum sharing problem with restrictedspectrum resource from the primary users is further analyzed, the optimization model basedon Lagrange multiplier is established, and a fast iteration algorithm to obtain shared spectrumprice of the primary users based on gradient iteration algorithm is proposed. It can avoid thepredefinition of adjustment factor according to the communication parameters in theconventional linear iterative algorithm, and obtain the shared spectrum price that satisfies theconstraints quickly. Besides, an improved spectrum leasing algorithm with the considerationof user requirements is proposed. It combines the competitive price game and the auctionmechanism, and can be applied in the spectrum sharing of multiple primary users andmultiple secondary users.
Finally, the spectrum access in cognitive radio networks with the consideration ofspectrum sensing revenue and the spectrum sensing overhead is analyzed. A spectrum accessalgorithm based on the spare probability of licensed spectrum bands is proposed. Based on thespare probability of different spectrum bands, with the adequate selection of collaborativecognitive users in spectrum sensing, the licensed spectrum bands with higher spareprobability is sensed with priority, and the transmission performance of the secondary users isalso taken into account. The improved spectrum access algorithm can reduce the spectrumsensing overhead and achieve better spectrum sensing revenue, and it can improve the spectrum access opportunity in cognitive radio networks efficiently.
本文主要研究认知无线电网络频谱资源分配与共享技术，其主要内容如下：
首先，分析了认知无线电通信系统的频谱共享问题，利用图着色模型讨论了填充式共享的认知无线电网络中的频谱分配算法。针对网络中认知节点移动对应重新计算认知用户的频谱分配矩阵的问题，提出一种改进的基于图着色模型的频谱分配算法，该算法建立在已获得频谱分配的基础上，通过引起干扰冲突的认知节点退出相应授权频带的占用及寻找空闲授权频带，降低了节点移动对频谱分配的影响。同时，分析了智能算法在认知无线电网络频谱分配中的应用，针对传统遗传算法的频谱分配模型求解过程中约束条件的处理进行深入分析，提出了一种基于约束模板的认知无线电网络遗传算法频谱分配模型。该算法根据认知用户间干扰关系，以系统效用最大化为目标产生无干扰的约束模板，再以约束模板的组合构成染色体，通过遗传算法进化得到使系统效用最大的约束模板组合，并对认知无线电网络频谱分配算法的性能进行比较与分析。
其次，分析了基于覆盖式共享的认知无线电网络中的功率分配问题，提出一种基于博弈论认知无线电网络分步功率分配算法，将认知无线电网络的功率分配问题分解为目标信干比的求解与功率分配方案的求解，该算法可以在获得相似系统吞吐量的条件下降低认知用户数据传输的功率消耗水平。同时，为了更好地描述认知用户通信对授权用户通信质量的影响，分析了基于授权用户反馈控制信息的频谱共享模型，提出一种基于授权用户反馈控制信息的改进频谱共享算法。该算法结合最优化理论与反馈控制信息，相比传统算法可快速迭代求解认知用户的功率分配策略，更好地保证授权用户的中断概率性能。
再次，分析了基于频谱租赁机制的认知无线电网络的频谱共享问题，主要探讨了基于竞价博弈的及拍卖机制的频谱资源共享问题。分析了授权用户可提供频谱资源受限条件下的竞价频谱共享问题，利用拉格朗日乘子法建立了满足授权用户频谱资源约束条件的优化模型，并利用结合最速梯度迭代算法提出了一种快速求解授权用户共享频谱价格策略的方法，可避免传统线性迭代算法中预先根据用户通信参数确定调整因子的环节，快速得到满足约束条件的共享频谱价格。同时，结合竞价博弈与拍卖机制提出一种考虑认知用户需求的分级频谱租赁模型，可用于分析具有多个授权用户与认知用户的网络模型中的频谱租赁问题。
最后，分析了认知无线电网络中的频谱接入技术，讨论了认知无线电网络中的频谱感知收益与频谱感知开销问题，提出了一种基于授权频带空闲概率模型的认知用户频谱接入算法，以不同授权频带的空闲概率为基础，合理选择参与协作感知的认知用户，优先对概率模型中空闲概率高的授权频带进行感知，考虑了认知用户的传输特性，可降低频谱感知开销获得更高的频谱感知收益，更高效地寻找认知无线电网络中的频谱接入机会。
With the development of wireless communication technology, the demand for wirelessspectrum has been growing tremendously, and leads to increasing scarcity in wirelessspectrum resource. Although the advanced wireless communication techniques are applied,such as the link adaptation and multi-antenna technology, which can improve the spectrumefficiency to some extent, the increasing demand of wireless spectrum resource requirementsstill can’t be satisfied. However, according to the research results, the spectrum resource in theexisting static spectrum sharing is not fully utilized. The cognitive radio technology hasattracted tremendous attention, and becomes an effective technique to alleviate thecontradiction between the scarcity and low efficiency of the existing static spectrum sharingscheme.
This paper focuses on spectrum allocation and sharing of the cognitive radio networks, itmainly includes:
Firstly, the spectrum sharing problem of the cognitive radio communication system isanalyzed. The list coloring algorithm is applied to discuss the spectrum allocation problem inthe overlay cognitive radio networks. In order to avoid the recalculation of the spectrumallocation with the mobility of the cognitive users, an improved list coloring based spectrumallocation algorithm is proposed. On the basis of previous spectrum allocation, it reduces theimpact of secondary users’ mobility to the spectrum allocation by releasing correspondingconflicted channels and searching for available channels. In addition, the spectrum allocationbased on intelligent algorithm is further analyzed, and the constraint templates based geneticspectrum assignment model for cognitive radio networks is proposed. The noninterferenceconstraint templates are calculated aiming at maximizing the system utility, and thechromosome in the proposed algorithm is composed of the indexes of the constraint templates.Thus the combination of the constraint templates with high fitness is obtained through theevolution of genetic operators, and the corresponding feasible spectrum assignment strategywithout interference can be achieved. The performance of the spectrum assignmentalgorithms for cognitive radio networks are compared and evaluated by simulation results.
Secondly, the power distribution in the cognitive radio networks based on underlay spectrum sharing model is analyzed, and a step power distribution algorithm based on gametheory is proposed. The power distribution of the cognitive radio networks can be divided into the calculation of target signal to noise ratio and the calculation of power distribution. Itcan achieve the similar throughput performance with lower consumed power levels.Meanwhile, in order to control the impact of secondary transmission on the communicationquality of licensed users, the spectrum sharing model based on the feedback controlinformation is analyzed, and an improve power distribution algorithm based on feedbackcontrol information from the primary user is proposed. It combines the optimization theoryand feedback control information, and can obtain the power distribution strategies with lessiteration than the conventional algorithm. It can also get better outage probabilityperformance of the primary users.
Thirdly, the spectrum sharing problem based on spectrum leasing mechanism incognitive radio networks is analyzed, the spectrum sharing models based on competitive pricegame and auction mechanism are discussed. The spectrum sharing problem with restrictedspectrum resource from the primary users is further analyzed, the optimization model basedon Lagrange multiplier is established, and a fast iteration algorithm to obtain shared spectrumprice of the primary users based on gradient iteration algorithm is proposed. It can avoid thepredefinition of adjustment factor according to the communication parameters in theconventional linear iterative algorithm, and obtain the shared spectrum price that satisfies theconstraints quickly. Besides, an improved spectrum leasing algorithm with the considerationof user requirements is proposed. It combines the competitive price game and the auctionmechanism, and can be applied in the spectrum sharing of multiple primary users andmultiple secondary users.
Finally, the spectrum access in cognitive radio networks with the consideration ofspectrum sensing revenue and the spectrum sensing overhead is analyzed. A spectrum accessalgorithm based on the spare probability of licensed spectrum bands is proposed. Based on thespare probability of different spectrum bands, with the adequate selection of collaborativecognitive users in spectrum sensing, the licensed spectrum bands with higher spareprobability is sensed with priority, and the transmission performance of the secondary users isalso taken into account. The improved spectrum access algorithm can reduce the spectrumsensing overhead and achieve better spectrum sensing revenue, and it can improve the spectrum access opportunity in cognitive radio networks efficiently.
引文
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