认知无线电中合作频谱感知方法研究
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摘要
随着无线通信技术及其应用业务的飞速发展,使得适合于无线通信的频谱资源变得日益紧张,这已成为制约无线通信技术发展的瓶颈,而认知无线电技术是解决这个问题的最佳方案。频谱感知使得认知无线电具有检测、学习及感知各种无线电磁参数的能力,是认知无线电技术能否实现实际应用的前提与基础,是认知无线电技术中的关键技术之一。
本文正是以认知无线电技术为背景,重点对中心式的合作频谱感知算法进行了研究。在对传统频谱感知算法深入研究的基础上,针对它们的某些缺点,借助数学上的合作博弈理论以及随机矩阵理论等工具对新的合作频谱感知技术展开了研究,并取得了一些研究成果。其主要研究成果如下:
(1)根据渐进谱理论中有关随机矩阵最大特征的统计特性,提出了一种基于双特征值判决门限的合作频谱感知算法。
利用多个认知用户接收到的主用户发射机信号产生一个采样协方差矩阵,该协方差矩阵包含了主用户信号源的相关信息。在主用户发射机信号存在与不存在两种假设条件下,依据渐近谱理论推出这两种假设情况下采样协方差矩阵的最大特征值,并以这两个最大特征值作为判决门限,实现对某段频谱内是否存在主用户发射机信号做出判决。
(2)针对基于最大特征值的频谱感知算法对噪声敏感的缺点,提出了一种基于最大特征值与平均能量比值的合作频谱感知算法。
该算法将认知用户接收信号协方差矩阵的最大特征值与接收信号的平均能量的比值作为检验统计量,并由采样协方差矩阵的最大特征值的极限、平均能量分布特性以及虚警概率求得判决门限,该判决门限与噪声没有任何关系且无需任何有关主用户发射机信号的先验知识。该算法克服了基于最大特征值的频谱感知算法对噪声敏感的缺点,进一步提高了整个系统的感知性能及其可靠性。
(3)构建了一种合作频谱感知博弈模型,并在此基础上提出了一种基于一般化纳什谈判解策略的合作频谱感知新方法。
在有中心节点的多认知用户合作频谱感知场景中,由于每一认知用户所处的空间位置以及无线电磁环境的不同,使得每个认知用户接收机的信噪比及判决门限均不相同。因此,每一认知用户在数据融合时将具有不同的感知贡献和权重。基于以上事实,本方法利用合作博弈论中的一般化纳什谈判解策略,构造出较符合实际情况的两认知用户合作感知的纳什谈判解。采用感知可信度来表征距离及信道参数等对感知可靠性的影响,并通过最优化方法求得两认知用户合作感知情形下,每个认知用户在感知结果中所占的权重,进而求出此情形下的合作频谱感知性能。对于多认知用户的情况,先采用指派分组算法将多认知用户分成两俩一组,然后同样利用一般化纳什谈判解策略求出每一组的合作感知性能。最后求出整个系统的感知性能。
(4)根据认知无线电中授权频谱被占用情况的统计On-Off模型,提出了一种基于贝叶斯判决规则的异步合作频谱感知算法。
该算法考虑到主用户行为会对认知用户的检测性能产生影响,以及在实际的合作频谱感知环境中,每个认知用户感知信息的获取时间不尽相同,而且各个认知用户将感知信息传送给融合中心的时间也不可能同步。本算法将每个认知用户在不同时刻的软判决结果传输给数据融合中心,在考虑到不同认知节点具有不同信噪比的情况下,将On-Off模型求得的贝叶斯判决准则似然比作为感知权重,对各认知用户的感知结果进行数据融合。由于算法考虑到了合作感知过程中每个认知用户感知时刻不同步、传输数据异步的实际特点,因此,与传统的同步合作频谱感知算法相比,该算法的合理性及可靠性更强,感知性能也更高。
With the rapid development of wireless communications technologies and the applications, the last decade has witnessed the growing demand for wireless radio spectrum. This has resulted in that wireless spectrum has been increasingly scarce, which has led to the obstacle to the improvement of wireless communications technologies. Cognitive Radio (CR) is the optimal choice to solve the above problem. Spectrum sensing enables the capability of CR to detect, learn and be aware of the wireless electromagnetic parameters, and it determines whether CR can be applied to the practical system or not, is one of the key techniques for cognitive radio.
In this dissertation, the centralized cooperative spectrum sensing algorithms are mainly focused on, and the disadvantages of the traditional spectrum sensing methods are deeply investigated. In order to overcome the disadvantages of the conventional spectrum sensing algorithms, this dissertation has explored some novel cooperative spectrum sensing algorithms based on cooperative game theory and random matrix theory, and has acquired some research fruits as follows.
(1) Based on the distribution of the maximum eigenvalue of random matrix in asymptotic spectrum theory, a DET (Double Eigenvalue Threshold) cooperative spectrum sensing algorithm is proposed.
DET can find the two maximum eigenvalues for hypotheses H0 (signal does not exist) and H1 (signal exists) by analysis of the sample covariance matrix of the received signals using Asymptotic Spectrum Theory (AST). The two maximum eigenvalues are regarded as two thresholds to decide whether the transmitted primary user signal is present or not.
(2) In order to overcome the disadvantages that the maximum eigenvalue based algorithms are sensitive to the noise uncertainty, a ME-ED (Maximum Eigenvalue-Energy Detection) is presented in this dissertation.
The proposed algorithm exploits the ratio of the Maximum Eigenvalue to Energy Detection (ME-ED) to determine whether the Primary User (PU) is absent or not. Through the theoretical analyses, ME-ED scheme can work well without the knowledge of the PU priori and the noise power. In addition, ME-ED algorithm is not sensitive to noise uncertainty at all, and can further improve the sensing performance and robustness.
(3) A cooperative spectrum sensing game model has been constructed, based on which a novel cooperative spectrum sensing approach using General Nash Bargaining Solution (GNBS) is presented.
In the centralized cooperative spectrum sensing scenario, different cognitive users (Secondary Users, SUs) have different average SNRs and different decision thresholds due to the different spatial position and wireless electromagnetic environment for each SU. Therefore, each SU contributes differently to the final sensing result, and has different weight at the fusion center. According to the aforementioned circumstances, the proposed algorithm exploits GNBS in Cooperative Game Theory (CGT) to construct a two-user cooperative sensing Nash bargaining solution problem. In the proposed scheme, Sensing creditability degree is used for characterizing effects of the distance and channel parameters on the sensing creditability, and the sensing performance for two-user case is derived by using the optimization method. For multi-user case, all SUs are grouped into pairs called coalitions with the assignment method, and for each pair, the sensing performance is obtained based on the two-user method. Finally, the sensing results for each pair are weighted at the fusion center to acquire the final sensing performance.
(4) A novel asynchronous cooperative spectrum sensing (ACSS) algorithm is presented in this dissertation, which is based on the occupancy model of licensed band and Bayesian decision rule.
In practice, each secondary user (SU) can not synchronously obtain the sensing information and make the decision, and the sensing data from all SUs can not be transmitted to the data fusion center at the same time. What’s more, primary users’behaviors which can be modeled as the On-Off model also affect the detection performance of CR. The proposed algorithm ACSS takes all these factors into account. In ACSS, the asynchronous soft decision results from secondary users are transmitted to the fusion center, and these soft decisions are weighted based on the likelihood ratio from Bayesian decision rule and combined at the fusion center. In addition, the fact that each SU has different SNR is considered in ACSS. Compared with the traditional synchronous cooperative spectrum sensing (SCSS) algorithms, the proposed algorithm is theoretically more reasonable, more credible and more suitable to the practical situation.
本文正是以认知无线电技术为背景,重点对中心式的合作频谱感知算法进行了研究。在对传统频谱感知算法深入研究的基础上,针对它们的某些缺点,借助数学上的合作博弈理论以及随机矩阵理论等工具对新的合作频谱感知技术展开了研究,并取得了一些研究成果。其主要研究成果如下:
(1)根据渐进谱理论中有关随机矩阵最大特征的统计特性,提出了一种基于双特征值判决门限的合作频谱感知算法。
利用多个认知用户接收到的主用户发射机信号产生一个采样协方差矩阵,该协方差矩阵包含了主用户信号源的相关信息。在主用户发射机信号存在与不存在两种假设条件下,依据渐近谱理论推出这两种假设情况下采样协方差矩阵的最大特征值,并以这两个最大特征值作为判决门限,实现对某段频谱内是否存在主用户发射机信号做出判决。
(2)针对基于最大特征值的频谱感知算法对噪声敏感的缺点,提出了一种基于最大特征值与平均能量比值的合作频谱感知算法。
该算法将认知用户接收信号协方差矩阵的最大特征值与接收信号的平均能量的比值作为检验统计量,并由采样协方差矩阵的最大特征值的极限、平均能量分布特性以及虚警概率求得判决门限,该判决门限与噪声没有任何关系且无需任何有关主用户发射机信号的先验知识。该算法克服了基于最大特征值的频谱感知算法对噪声敏感的缺点,进一步提高了整个系统的感知性能及其可靠性。
(3)构建了一种合作频谱感知博弈模型,并在此基础上提出了一种基于一般化纳什谈判解策略的合作频谱感知新方法。
在有中心节点的多认知用户合作频谱感知场景中,由于每一认知用户所处的空间位置以及无线电磁环境的不同,使得每个认知用户接收机的信噪比及判决门限均不相同。因此,每一认知用户在数据融合时将具有不同的感知贡献和权重。基于以上事实,本方法利用合作博弈论中的一般化纳什谈判解策略,构造出较符合实际情况的两认知用户合作感知的纳什谈判解。采用感知可信度来表征距离及信道参数等对感知可靠性的影响,并通过最优化方法求得两认知用户合作感知情形下,每个认知用户在感知结果中所占的权重,进而求出此情形下的合作频谱感知性能。对于多认知用户的情况,先采用指派分组算法将多认知用户分成两俩一组,然后同样利用一般化纳什谈判解策略求出每一组的合作感知性能。最后求出整个系统的感知性能。
(4)根据认知无线电中授权频谱被占用情况的统计On-Off模型,提出了一种基于贝叶斯判决规则的异步合作频谱感知算法。
该算法考虑到主用户行为会对认知用户的检测性能产生影响,以及在实际的合作频谱感知环境中,每个认知用户感知信息的获取时间不尽相同,而且各个认知用户将感知信息传送给融合中心的时间也不可能同步。本算法将每个认知用户在不同时刻的软判决结果传输给数据融合中心,在考虑到不同认知节点具有不同信噪比的情况下,将On-Off模型求得的贝叶斯判决准则似然比作为感知权重,对各认知用户的感知结果进行数据融合。由于算法考虑到了合作感知过程中每个认知用户感知时刻不同步、传输数据异步的实际特点,因此,与传统的同步合作频谱感知算法相比,该算法的合理性及可靠性更强,感知性能也更高。
With the rapid development of wireless communications technologies and the applications, the last decade has witnessed the growing demand for wireless radio spectrum. This has resulted in that wireless spectrum has been increasingly scarce, which has led to the obstacle to the improvement of wireless communications technologies. Cognitive Radio (CR) is the optimal choice to solve the above problem. Spectrum sensing enables the capability of CR to detect, learn and be aware of the wireless electromagnetic parameters, and it determines whether CR can be applied to the practical system or not, is one of the key techniques for cognitive radio.
In this dissertation, the centralized cooperative spectrum sensing algorithms are mainly focused on, and the disadvantages of the traditional spectrum sensing methods are deeply investigated. In order to overcome the disadvantages of the conventional spectrum sensing algorithms, this dissertation has explored some novel cooperative spectrum sensing algorithms based on cooperative game theory and random matrix theory, and has acquired some research fruits as follows.
(1) Based on the distribution of the maximum eigenvalue of random matrix in asymptotic spectrum theory, a DET (Double Eigenvalue Threshold) cooperative spectrum sensing algorithm is proposed.
DET can find the two maximum eigenvalues for hypotheses H0 (signal does not exist) and H1 (signal exists) by analysis of the sample covariance matrix of the received signals using Asymptotic Spectrum Theory (AST). The two maximum eigenvalues are regarded as two thresholds to decide whether the transmitted primary user signal is present or not.
(2) In order to overcome the disadvantages that the maximum eigenvalue based algorithms are sensitive to the noise uncertainty, a ME-ED (Maximum Eigenvalue-Energy Detection) is presented in this dissertation.
The proposed algorithm exploits the ratio of the Maximum Eigenvalue to Energy Detection (ME-ED) to determine whether the Primary User (PU) is absent or not. Through the theoretical analyses, ME-ED scheme can work well without the knowledge of the PU priori and the noise power. In addition, ME-ED algorithm is not sensitive to noise uncertainty at all, and can further improve the sensing performance and robustness.
(3) A cooperative spectrum sensing game model has been constructed, based on which a novel cooperative spectrum sensing approach using General Nash Bargaining Solution (GNBS) is presented.
In the centralized cooperative spectrum sensing scenario, different cognitive users (Secondary Users, SUs) have different average SNRs and different decision thresholds due to the different spatial position and wireless electromagnetic environment for each SU. Therefore, each SU contributes differently to the final sensing result, and has different weight at the fusion center. According to the aforementioned circumstances, the proposed algorithm exploits GNBS in Cooperative Game Theory (CGT) to construct a two-user cooperative sensing Nash bargaining solution problem. In the proposed scheme, Sensing creditability degree is used for characterizing effects of the distance and channel parameters on the sensing creditability, and the sensing performance for two-user case is derived by using the optimization method. For multi-user case, all SUs are grouped into pairs called coalitions with the assignment method, and for each pair, the sensing performance is obtained based on the two-user method. Finally, the sensing results for each pair are weighted at the fusion center to acquire the final sensing performance.
(4) A novel asynchronous cooperative spectrum sensing (ACSS) algorithm is presented in this dissertation, which is based on the occupancy model of licensed band and Bayesian decision rule.
In practice, each secondary user (SU) can not synchronously obtain the sensing information and make the decision, and the sensing data from all SUs can not be transmitted to the data fusion center at the same time. What’s more, primary users’behaviors which can be modeled as the On-Off model also affect the detection performance of CR. The proposed algorithm ACSS takes all these factors into account. In ACSS, the asynchronous soft decision results from secondary users are transmitted to the fusion center, and these soft decisions are weighted based on the likelihood ratio from Bayesian decision rule and combined at the fusion center. In addition, the fact that each SU has different SNR is considered in ACSS. Compared with the traditional synchronous cooperative spectrum sensing (SCSS) algorithms, the proposed algorithm is theoretically more reasonable, more credible and more suitable to the practical situation.
引文
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