基于压缩感知的宽带频谱感知技术研究
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摘要
认知无线电在软件无线电的基础上发展而来，能够通过对外界频谱环境的持续感知，及时发现并使用授权频段内尚未被主用户利用的空闲频谱，是一种可以有效提高频谱资源利用效率的智能无线电技术，也能够有效缓解随着无线通信用户数量急剧增加和无线通信技术迅速发展而变得日益严峻的频谱资源稀缺问题。频谱感知技术是构建认知无线电系统、实现认知无线电应用的核心技术，也是保证认知无线电系统发现并充分利用频谱资源、保护授权主用户免受有害干扰的重要前提。随着无线通信业务的高速发展，对无线频谱资源需求急剧增加，为了进一步提高对于频谱资源的利用，宽带频谱感知技术成为认知无线电领域的重要研究方向。
在宽带认知无线电系统中，由于主用户通常对宽带频谱利用率较低，只占用其中少量频谱资源，因此主用户信号在频域上具有稀疏性，可以利用压缩感知实现宽带频谱感知。宽带压缩频谱感知技术利用信号的频域稀疏特性，通过随机测量将高维信号投影到低维测量结果，并采用优化算法准确重构原信号，从而能够以远低于奈奎斯特准则的采样速率直接扫描宽带频谱，在降低认知用户采样负担的同时实现高效快速的宽带频谱感知。然而，宽带压缩频谱感知技术仍面临一些挑战：首先，无线衰落环境会造成隐终端问题，影响宽带压缩频谱感知的检测性能；其次，宽带频谱稀疏度先验信息受限会使认知用户在进行宽带压缩频谱感知时难以确定合适的采样速率，进而导致采样开销的浪费或是导致感知性能的下降；此外，认知无线电系统中可能存在一些恶意认知用户，通过在数据融合过程中发送虚假的感知信息来攻击系统，使宽带压缩频谱感知的性能严重降低。
为解决上述问题，本文研究了在集中式认知无线电网络和分布式认知无线电网络中，如何通过结合压缩感知理论和多认知用户联合频谱感知技术来提高系统在无线衰落环境下对于宽带频谱的感知性能。针对主用户信号稀疏度先验信息受限的情况，本文基于顺序压缩感知提出能够自适应确定最佳随机测量次数的宽带频谱感知算法。此外，对恶意认知用户攻击下的可靠宽带压缩频谱感知算法也进行了研究。本文主要研究内容和成果列举如下：
首先，本文对传统的窄带频谱感知技术进行介绍，并指出感知宽带频谱时认知无线电系统所面临的挑战。在详细介绍压缩感知原理的基础上，分析宽带压缩频谱感知技术特点。针对由无线衰落环境引起的隐终端问题，提出能够解决该问题的多用户联合频谱感知方法。最后对本文重点研究的多认知用户集中式和分布式宽带认知无线电网络结构进行详细分析，并给出了衡量宽带压缩频谱感知的性能指标，为本文研究工作的展开进行了铺垫。
其次，为了解决信道衰落和噪声环境所造成的隐终端问题，以及感知宽带频谱所面临的采样负担过大的问题，本文针对所提出的集中式和分布式的宽带认知无线电网络频谱感知场景，在压缩感知理论基础上分别给出了集中式协作和分布式协作的宽带频谱感知算法。认知用户以远低于奈奎斯特准则的速率对宽带信号进行采样，并通过协作获得空间分集增益，有效缓解无线衰落环境对感知性能所造成的不利影响。同时，考虑到各个认知用户本地待重构信号频谱的联合稀疏特性，系统利用迭代支持检测技术从低维的压缩测量结果中准确重构原信号，并提升频谱感知性能。仿真表明，本文算法能够降低系统的采样负担，与基于基追踪和基于分布式一致的常规压缩频谱感知算法相比，能够更有效消除衰落环境的不利影响，更准确地感知宽带频谱。
再次，在宽带压缩频谱感知过程中，宽带频谱稀疏度先验信息受限将导致认知无线电系统在压缩感知时采用过高或过低的采样速率对信号进行随机测量，进而产生对采样资源的浪费、或者对原信号的重构不够准确等问题。针对这一问题，本文结合顺序压缩感知理论和自适应稀疏度匹配追踪技术，提出一种新的宽带压缩频谱感知算法。该算法通过顺序测量依次获得测量结果，再根据重构误差来自适应地确定成功重构原信号所需要的最佳随机测量次数。仿真表明在事先无法确知宽带频谱实际稀疏度的情况下，相比基于压缩采样匹配追踪的常规压缩频谱感知算法，本文算法在合理利用系统资源、避免不必要的采样开销的同时，能够确保系统的频谱感知性能。
最后，由于具有开放性和可配置型，认知无线电系统在现实中可能会受到恶意认知用户的攻击，其中常见的攻击方式是频谱感知数据伪造攻击。为了解决这一问题，本文分析了认知无线电网络中常见的攻击方式，并将声望系统引入认知无线电系统，针对集中式认知无线电网络提出了可靠的宽带压缩频谱感知算法，根据认知用户在联合频谱感知过程中的表现确定用户声望和权重，消除恶意用户的影响，保证系统感知性能。对于分布式认知无线电网络，本文在共识算法的基础上提出根据动态门限来调整融合权重的宽带压缩频谱感知方法。仿真表明通过结合压缩感知技术，本文算法能够有效减少认知用户感知宽带频谱的采样开销，并且在受到恶意认知用户攻击的集中式和分布式宽带认知无线电网络中都能够实现可靠准确的宽带频谱感知。
Developing based on software-defined radio, cognitive radio is an intelligent radio technology which is able to discover and utilize idle authorized spectrum which is allocated to primary user but not being used temporarily, via sensing external wireless spectrum environment persistently. Cognitive radio can efficiently solve the growing problem of spectrum resource scarcity caused by the rapid development of wireless communication technology and fast growth of wireless users. As the core technology of constructing and implementing cognitive radio, spectrum sensing enables cognitive radio system to discover and utilize spectrum resource, while protecting authorized primary user from harmful interference. As the rapid increase of wireless services and growing need of wireless spectrum resource, in order to improve the utilization of spectrum resource, wide-band spectrum sensing has become an important research direction in the field of cognitive radio.
In wide-band cognitive radio systems, since the utilization of wide-band spectrum is very low, and primary users only occupy a small amount of spectrum, the signal in frequency domain has sparsity. Compressive spectrum sensing utilizes such sparsity, projects high-dimensional signals on low-dimensional measurements, and reconstructs original signals using optimization algorithms. In this way, wide-band spectrum can be scanned directly at sub-Nyquist rates, and efficient wide-band spectrum sensing can be realized. However, wide-band compressive spectrum sensing encounters many challenges: First, hidden terminal problem is brought by wireless fading environment, and harms the detection performance of wide-band spectrum sensing; Second, because of the lack of prior knowledge on sparsity order, cognitive users will have difficulties in deciding accurate sampling rates while sensing the wide-band spectrum, which will lead to sampling wastage or poor sensing performance; In addition, in the cognitive radio system there may exist some malicious cognitive users, who will attack the system by sending false spectrum sensing data during the data fusion process, which will impair the performance of wide-band spectrum sensing seriously.
In order to solve above problems, this dissertation research on how to improve the performance of wide-band spectrum sensing using compressive sensing theory and collaborative spectrum sensing technology, in both centralized and distributed cognitive radio networks. Based on sequential compressive sensing, a wide-band spectrum sensing algorithm which can adaptively determine the optimal number of random measurements is proposed to solve the problem of sensing without the prior knowledge of the sparsity order. Additionally, a reliable wide-band compressive spectrum sensing is proposed to defend malicious users. The main work and contributions of this dissertation are as follows:
Firstly, this dissertation introduces traditional narrow-band spectrum sensing techniques, and analyzes the challenges faced when sensing wide-band spectrum. Compressive sensing theory is introduced, and the technical characteristics of wide-band spectrum sensing are analyzed. Then collaborative spectrum sensing technology, which can solve the hidden terminal problem, is introduced. This dissertation has proposed a detailed analysis on the structures of multi-user centralized and distributed wide-band cognitive radio networks, and provided specifications to evaluate the performance of wide-band compressive spectrum sensing.
Secondly, in order to solve the hidden terminal problem and reduce the burden of high sampling rates when sensing wide-band spectrum, this dissertation has proposed both centralized and distributed wide-band compressive spectrum sensing algorithms respectively for centralized and distributed cognitive networks. Cognitive users sample wide-band signals at sub-Nyquist rates, and gain spatial diversity gain via collaboration to relieve the negative influence caused by fading environment. In addition, original signals are reconstructed utilizing joint sparse property via iterative support detection. Simulations show that the proposed algorithms can reduce the sampling burden, and have better wide-band detection performances than conventional compressive spectrum sensing methods.
Thirdly, in the process of wide-band compressive spectrum sensing, the lack of prior knowledge of wide-band spectrum sparsity order will cause the system to employ overmuch or inadequate random measurements, which leads to sampling wastage or poor sensing performance. To solve this problem, this dissertation has proposed a novel wide-band compressive spectrum sensing algorithm based on sequential compressive sensing and sparsity adaptive matching pursuit. By obtaining sequential random measurements and reconstruction error, minimal number of random measurements can be determined. Simulations show that the proposed algorithm can utilize system resource efficiently, and achieve the desired spectrum sensing performance while avoiding the sampling wastage.
Last but not the least, due to the openness and configurability, cognitive radio system may encounter spectrum sensing data falsification attacks by malicious cognitive users. To solve this problem, common forms of attack are analyzed. A reputation-based algorithm is proposed for centralized cognitive radio networks, where fusion center uses the reputation of cognitive users to determine their weights in data fusion, and eliminates the negative influence caused by malicious users. As for distributed cognitive radio networks, a consensus-based wide-band compressive spectrum sensing algorithm using dynamic threshold to adjust fusion weights is proposed. Simulations show that the proposed algorithms can both effectively reduce sampling costs, successfully combat spectrum sensing data falsify attacks, and achieve accurate and reliable wide-band spectrum sensing in respectively centralized and distributed cognitive networks.
在宽带认知无线电系统中，由于主用户通常对宽带频谱利用率较低，只占用其中少量频谱资源，因此主用户信号在频域上具有稀疏性，可以利用压缩感知实现宽带频谱感知。宽带压缩频谱感知技术利用信号的频域稀疏特性，通过随机测量将高维信号投影到低维测量结果，并采用优化算法准确重构原信号，从而能够以远低于奈奎斯特准则的采样速率直接扫描宽带频谱，在降低认知用户采样负担的同时实现高效快速的宽带频谱感知。然而，宽带压缩频谱感知技术仍面临一些挑战：首先，无线衰落环境会造成隐终端问题，影响宽带压缩频谱感知的检测性能；其次，宽带频谱稀疏度先验信息受限会使认知用户在进行宽带压缩频谱感知时难以确定合适的采样速率，进而导致采样开销的浪费或是导致感知性能的下降；此外，认知无线电系统中可能存在一些恶意认知用户，通过在数据融合过程中发送虚假的感知信息来攻击系统，使宽带压缩频谱感知的性能严重降低。
为解决上述问题，本文研究了在集中式认知无线电网络和分布式认知无线电网络中，如何通过结合压缩感知理论和多认知用户联合频谱感知技术来提高系统在无线衰落环境下对于宽带频谱的感知性能。针对主用户信号稀疏度先验信息受限的情况，本文基于顺序压缩感知提出能够自适应确定最佳随机测量次数的宽带频谱感知算法。此外，对恶意认知用户攻击下的可靠宽带压缩频谱感知算法也进行了研究。本文主要研究内容和成果列举如下：
首先，本文对传统的窄带频谱感知技术进行介绍，并指出感知宽带频谱时认知无线电系统所面临的挑战。在详细介绍压缩感知原理的基础上，分析宽带压缩频谱感知技术特点。针对由无线衰落环境引起的隐终端问题，提出能够解决该问题的多用户联合频谱感知方法。最后对本文重点研究的多认知用户集中式和分布式宽带认知无线电网络结构进行详细分析，并给出了衡量宽带压缩频谱感知的性能指标，为本文研究工作的展开进行了铺垫。
其次，为了解决信道衰落和噪声环境所造成的隐终端问题，以及感知宽带频谱所面临的采样负担过大的问题，本文针对所提出的集中式和分布式的宽带认知无线电网络频谱感知场景，在压缩感知理论基础上分别给出了集中式协作和分布式协作的宽带频谱感知算法。认知用户以远低于奈奎斯特准则的速率对宽带信号进行采样，并通过协作获得空间分集增益，有效缓解无线衰落环境对感知性能所造成的不利影响。同时，考虑到各个认知用户本地待重构信号频谱的联合稀疏特性，系统利用迭代支持检测技术从低维的压缩测量结果中准确重构原信号，并提升频谱感知性能。仿真表明，本文算法能够降低系统的采样负担，与基于基追踪和基于分布式一致的常规压缩频谱感知算法相比，能够更有效消除衰落环境的不利影响，更准确地感知宽带频谱。
再次，在宽带压缩频谱感知过程中，宽带频谱稀疏度先验信息受限将导致认知无线电系统在压缩感知时采用过高或过低的采样速率对信号进行随机测量，进而产生对采样资源的浪费、或者对原信号的重构不够准确等问题。针对这一问题，本文结合顺序压缩感知理论和自适应稀疏度匹配追踪技术，提出一种新的宽带压缩频谱感知算法。该算法通过顺序测量依次获得测量结果，再根据重构误差来自适应地确定成功重构原信号所需要的最佳随机测量次数。仿真表明在事先无法确知宽带频谱实际稀疏度的情况下，相比基于压缩采样匹配追踪的常规压缩频谱感知算法，本文算法在合理利用系统资源、避免不必要的采样开销的同时，能够确保系统的频谱感知性能。
最后，由于具有开放性和可配置型，认知无线电系统在现实中可能会受到恶意认知用户的攻击，其中常见的攻击方式是频谱感知数据伪造攻击。为了解决这一问题，本文分析了认知无线电网络中常见的攻击方式，并将声望系统引入认知无线电系统，针对集中式认知无线电网络提出了可靠的宽带压缩频谱感知算法，根据认知用户在联合频谱感知过程中的表现确定用户声望和权重，消除恶意用户的影响，保证系统感知性能。对于分布式认知无线电网络，本文在共识算法的基础上提出根据动态门限来调整融合权重的宽带压缩频谱感知方法。仿真表明通过结合压缩感知技术，本文算法能够有效减少认知用户感知宽带频谱的采样开销，并且在受到恶意认知用户攻击的集中式和分布式宽带认知无线电网络中都能够实现可靠准确的宽带频谱感知。
Developing based on software-defined radio, cognitive radio is an intelligent radio technology which is able to discover and utilize idle authorized spectrum which is allocated to primary user but not being used temporarily, via sensing external wireless spectrum environment persistently. Cognitive radio can efficiently solve the growing problem of spectrum resource scarcity caused by the rapid development of wireless communication technology and fast growth of wireless users. As the core technology of constructing and implementing cognitive radio, spectrum sensing enables cognitive radio system to discover and utilize spectrum resource, while protecting authorized primary user from harmful interference. As the rapid increase of wireless services and growing need of wireless spectrum resource, in order to improve the utilization of spectrum resource, wide-band spectrum sensing has become an important research direction in the field of cognitive radio.
In wide-band cognitive radio systems, since the utilization of wide-band spectrum is very low, and primary users only occupy a small amount of spectrum, the signal in frequency domain has sparsity. Compressive spectrum sensing utilizes such sparsity, projects high-dimensional signals on low-dimensional measurements, and reconstructs original signals using optimization algorithms. In this way, wide-band spectrum can be scanned directly at sub-Nyquist rates, and efficient wide-band spectrum sensing can be realized. However, wide-band compressive spectrum sensing encounters many challenges: First, hidden terminal problem is brought by wireless fading environment, and harms the detection performance of wide-band spectrum sensing; Second, because of the lack of prior knowledge on sparsity order, cognitive users will have difficulties in deciding accurate sampling rates while sensing the wide-band spectrum, which will lead to sampling wastage or poor sensing performance; In addition, in the cognitive radio system there may exist some malicious cognitive users, who will attack the system by sending false spectrum sensing data during the data fusion process, which will impair the performance of wide-band spectrum sensing seriously.
In order to solve above problems, this dissertation research on how to improve the performance of wide-band spectrum sensing using compressive sensing theory and collaborative spectrum sensing technology, in both centralized and distributed cognitive radio networks. Based on sequential compressive sensing, a wide-band spectrum sensing algorithm which can adaptively determine the optimal number of random measurements is proposed to solve the problem of sensing without the prior knowledge of the sparsity order. Additionally, a reliable wide-band compressive spectrum sensing is proposed to defend malicious users. The main work and contributions of this dissertation are as follows:
Firstly, this dissertation introduces traditional narrow-band spectrum sensing techniques, and analyzes the challenges faced when sensing wide-band spectrum. Compressive sensing theory is introduced, and the technical characteristics of wide-band spectrum sensing are analyzed. Then collaborative spectrum sensing technology, which can solve the hidden terminal problem, is introduced. This dissertation has proposed a detailed analysis on the structures of multi-user centralized and distributed wide-band cognitive radio networks, and provided specifications to evaluate the performance of wide-band compressive spectrum sensing.
Secondly, in order to solve the hidden terminal problem and reduce the burden of high sampling rates when sensing wide-band spectrum, this dissertation has proposed both centralized and distributed wide-band compressive spectrum sensing algorithms respectively for centralized and distributed cognitive networks. Cognitive users sample wide-band signals at sub-Nyquist rates, and gain spatial diversity gain via collaboration to relieve the negative influence caused by fading environment. In addition, original signals are reconstructed utilizing joint sparse property via iterative support detection. Simulations show that the proposed algorithms can reduce the sampling burden, and have better wide-band detection performances than conventional compressive spectrum sensing methods.
Thirdly, in the process of wide-band compressive spectrum sensing, the lack of prior knowledge of wide-band spectrum sparsity order will cause the system to employ overmuch or inadequate random measurements, which leads to sampling wastage or poor sensing performance. To solve this problem, this dissertation has proposed a novel wide-band compressive spectrum sensing algorithm based on sequential compressive sensing and sparsity adaptive matching pursuit. By obtaining sequential random measurements and reconstruction error, minimal number of random measurements can be determined. Simulations show that the proposed algorithm can utilize system resource efficiently, and achieve the desired spectrum sensing performance while avoiding the sampling wastage.
Last but not the least, due to the openness and configurability, cognitive radio system may encounter spectrum sensing data falsification attacks by malicious cognitive users. To solve this problem, common forms of attack are analyzed. A reputation-based algorithm is proposed for centralized cognitive radio networks, where fusion center uses the reputation of cognitive users to determine their weights in data fusion, and eliminates the negative influence caused by malicious users. As for distributed cognitive radio networks, a consensus-based wide-band compressive spectrum sensing algorithm using dynamic threshold to adjust fusion weights is proposed. Simulations show that the proposed algorithms can both effectively reduce sampling costs, successfully combat spectrum sensing data falsify attacks, and achieve accurate and reliable wide-band spectrum sensing in respectively centralized and distributed cognitive networks.
引文
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