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0、引言
众所周知,无线电通信频谱是一种宝贵的自然资源,一般由政府授权使用。由于通信行业的迅速发展,频谱资源贫乏的问题日益严重,尤其是在频率需求非常紧张的数百MHz-3GHz无线频带中,一些频带大部分时间内并没有用户使用,另有一些偶尔才被占用,其他频带使用竞争则相对很激烈。怎样才能提高频谱利用率,在各地区和各个时间段里有效地利用不同的空闲频道, 相似文献
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在传统的无线通信系统中,频谱的分配是固定的。但是由于通信过程的突发性,这些频谱的使用率很低。另一方面,随着无线通信和多媒体的高速发展和广泛应用,无线频谱资源日趋紧张。如何提高频谱利用率已经成为迫切需要解决的问题。一种可行的思路是把这些授权频谱向未授权用户开放,未授权用户采用动态频谱接入技术,在不对授权用户造成干扰的前提下使用频谱。本文以认知无线电技术(Cognitive Radio,CR)为基础,提出了一种基于CR的动态频谱接入MAC方案(CR-Ad Hoc-MAC)。该方案允许未授权用户自适应地选取可用带宽,实现了动态频谱接入,有效地提高了频谱利用率。 相似文献
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根据认知无线电实际频谱需求,通过对弱信号检测技术的研究,该文首次在认知无线电领域提出了一种基于三重矩阵累积估计的频谱空穴检测算法,该算法将频域块自适应滤波与矩阵重构、累积估计和频域平滑相结合实现弱信号检测。最后以QPSK调制信号为例进行了算法的计算机仿真,给出了性能分析。仿真结果表明该检测算法能够快速有效地实现弱信号检测并具有较高的检测概率,完全可以应用于认知无线电的频谱空穴检测。 相似文献
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认知无线电和开放无线结构逐渐成为移动通信领域的研究热点,在移动通信系统中,终端也是决定整体性能的一个重要因素。文章介绍了认知无线电和开放无线结构,并基于认知无线电和开放无线结构提出了一种新的终端架构。 相似文献
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一种新的认知无线电频谱感知方法 总被引:1,自引:1,他引:0
认知无线电是一种用于提高无线电通信频谱利用率的新的智能技术。首先简述了认知无线电的背景和概念,然后针对认知无线电频谱感知的能力,对比分析了现有的三种频谱检测方法:匹配滤波器法、能量检查法和循环平稳特征检测法,在对其进行研究的基础上,提出一种将能量检测法和循环平稳特征检测法相结合的双门限检测法,通过仿真验证了该方法的有效性和优越性。 相似文献
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分析了认知无线电网络的集中式、Ad Hoc式和Mesh式体系架构及其节点与链路特性.在认知无线电终端方面,分析了系统组成,并阐述了其认知引擎中环境感知模块、方案制定与优化模块、知识库、知识进化器和接口模块的功能定义以及认知循环过程.最后,论述了认知无线电的频谱感知、频谱分配以及功率控制关键技术. 相似文献
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一种新的认知无线电频谱感知算法 总被引:3,自引:2,他引:1
频谱感知是认知无线电的关键技术之一,通过检测授权用户信号的有无来发现频谱空穴,以提高频谱利用率。基于接收信号的统计协方差理论,提出一种新的认知无线电频谱感知算法,无需信号的先验信息,且计算复杂度较低。仿真分析了该算法,结果表明其在低信噪比环境下,较传统能量检测法有更好的感知性能。 相似文献
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认知无线电目前已经成为现代无线通信技术研究的一项热点内容.认知无线电技术在现代通信中发挥着重要作用,因此对其相关内容对针对性分,对促进我国通信行业的发展来说意义重大. 相似文献
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文章简要阐述了无线智能认知技术的概念、原理及其主要技术。就当前大数据时代无线用户海量增加与频谱资源日趋紧张的矛盾,用无线知能认知技术分析了如何充分提高无线数据传输设备发现频谱空洞和提高频谱利用的能力。 相似文献
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Most resource allocation algorithms are based on interference power constraint in cognitive radio networks. Instead of using conventional primary user interference constraint, we give a new criterion called allowable signal to interference plus noise ratio (SINR) loss constraint in cognitive transmission to protect primary users. Considering power allocation problem for cognitive users over flat fading channels, in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user, we propose a new power allocation algorithm. The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks. 相似文献
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Today’s static spectrum allocation policy results in a situation where the available spectrum is being exhausted while many
licensed spectrum bands are under-utilized. To resolve the spectrum exhaustion problem, the cognitive radio wireless network,
termed CogNet in this paper, has recently been proposed to enable unlicensed users to dynamically access the licensed spectrum bands that
are unused in either temporal or spatial domain, through spectrum-agile cognitive radios. The CogNet plays the role of secondary
user in this shared spectrum access framework, and the spectrum bands accessible by CogNets are inherently heterogeneous and
dynamic. To establish the communication infrastructure for a CogNet, the cognitive radio of each CogNet node detects the accessible
spectrum bands and chooses one as its operating frequency, a process termed channel assignment. In this paper we propose a graph-based path-centric channel assignment framework to model multi-hop ad hoc CogNets and perform channel assignment from a network perspective.
Simulation results show that the path-centric channel assignment framework outperforms traditional link-centric approach.
Chunsheng Xin received the Ph.D. degree in computer science from State University of New York at Buffalo in 2002. From 2000 to 2002, he was a Research Co-Op in Nokia Research Center, Boston. From 2002, he is an assistant professor in the Computer Science Department, Norfolk State University, Norfolk, Virginia. His research interests include optical networks, cognitive radio wireless networks, and performance evaluation and modeling. Liangping Ma received his B.S. degree in Physics from Wuhan University, Hubei, China, in 1998, and his Ph.D. degree in Electrical Engineering from the University of Delaware, Newark, DE, in 2004. He was with the University of Delaware as a Postdoctoral Research Fellow. Since 2005, he has been with San Diego Research Center, Inc. (now part of Argon ST, Inc.), San Diego, CA, as a Research Staff Member. His research interests include medium access control (MAC), spectrum agile radios, and signal processing. Chien-Chung Shen received his B.S. and M.S. degrees from National Chiao Tung University, Taiwan, and his Ph.D. degree from UCLA, all in computer science. He was a senior research scientist at Bellcore (now Telcordia) Applied Research working on control and management of broadband networks. He is now an associate professor in the Department of Computer and Information Sciences of the University of Delaware, and a recipient of NSF CAREER Award. His research interests include ad hoc and sensor networks, dynamic spectrum management, control and management of broadband networks, distributed object and peer-to-peer computing, and simulation. He is a member of both ACM and IEEE. 相似文献
Chien-Chung ShenEmail: |
Chunsheng Xin received the Ph.D. degree in computer science from State University of New York at Buffalo in 2002. From 2000 to 2002, he was a Research Co-Op in Nokia Research Center, Boston. From 2002, he is an assistant professor in the Computer Science Department, Norfolk State University, Norfolk, Virginia. His research interests include optical networks, cognitive radio wireless networks, and performance evaluation and modeling. Liangping Ma received his B.S. degree in Physics from Wuhan University, Hubei, China, in 1998, and his Ph.D. degree in Electrical Engineering from the University of Delaware, Newark, DE, in 2004. He was with the University of Delaware as a Postdoctoral Research Fellow. Since 2005, he has been with San Diego Research Center, Inc. (now part of Argon ST, Inc.), San Diego, CA, as a Research Staff Member. His research interests include medium access control (MAC), spectrum agile radios, and signal processing. Chien-Chung Shen received his B.S. and M.S. degrees from National Chiao Tung University, Taiwan, and his Ph.D. degree from UCLA, all in computer science. He was a senior research scientist at Bellcore (now Telcordia) Applied Research working on control and management of broadband networks. He is now an associate professor in the Department of Computer and Information Sciences of the University of Delaware, and a recipient of NSF CAREER Award. His research interests include ad hoc and sensor networks, dynamic spectrum management, control and management of broadband networks, distributed object and peer-to-peer computing, and simulation. He is a member of both ACM and IEEE. 相似文献
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认知无线电是在软件无线电基础上发展而来的能够自适应外界环境变化的智能无线通信系统,其核心思想是使无线通信设备具有发现"频谱空穴"并合理利用所发现的"频谱空穴"的能力。认知无线电的提出为从根本上解决日益增长的无线通信需求与有限的无线频谱资源之间的矛盾开辟了一条行之有效的新途径。从认知无线电技术的研究背景入手,综合阐述了认知无线电的定义及其特点、实现认知无线电的关键技术以及超宽带与认知无线电相结合的认知超宽带通信技术。 相似文献
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认知无线电是一种基于软件无线电的智能的无线通信系统,它能够认知周围环境。并能够通过一定的方法相应地改变某些工作参数来实时地适应环境,从而达到提高频谱利用率、缓解频谱资源紧张的目的。认知无线电的首要任务是检测频谱的空洞。通常用在认知无线电中的非参数谱估计的方法主要包括多窗谱估计、Welch方法等。多窗谱估计算法在进行干扰温度的估计和频谱空洞的判定时,能够利用设立的多个传感器对环境信号进行接收和监测,并按照多窗谱估计与奇异值分解(MTM—SVD)算法进行处理获得干扰温度估计值,最后将其与干扰温度限比较判决,从而得到适合认知无线电系统应用的频谱空洞。 相似文献