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Compressive Covariance Sampling for Spectrum Sensing (CoCoSa)

用于频谱传感的压缩协方差采样 (CoCoSa)

基本信息

批准号：
260738363
负责人：
Professor Dr. Rudolf Mathar
金额：
--
依托单位：
Mathematisches Institut
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2016-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/260738363?language=en
关键词：
Compressive Covariance Sampling Spectrum Sensing

项目摘要

The available radio spectrum has become a scarce resource despite the fact that large parts of the licensed spectral bands are underutilized. Approaches to make more efficient use of the available spectrum have been developed within the framework of cognitive radio.The key idea is to let unlicensed radios access free spectral resources as long as they can ensure not to interfere with licensed usage. The methods enabling reliable licensed user detection, and thus safe secondary utilization, go by the name of spectrum sensing.The problem at hand is the detection of signals in very low signal to noise ratio (SNR) regimes. Multiple approaches have been put forward, some of which exploit the presence of inherent stochastic features in communication signals, e.g., properties of a signal's covariance matrix. However, to detect characteristic stochastic features of communication signals reliably, a large amount of measurement data has to be processed.To this end, we aim at developing methods and algorithms for licensed transmitter detection from a drastically reduced number of samples. Different types of covariance estimation shall be analyzed with respect to their error performance, and new detectors are to be developed. In particular, we will make a rigorous mathematical analysis on the minimal number of samples required for accurate covariance estimation under realistic assumptions on its structure. Typically, estimation of the covariance matrix and the choice of a test statistic for the binary hypothesis test (channel free or occupied) are treated independently. By interlocking estimation and detection approaches and associated test statistics new insights are to be expected due to the intended cooperation.In concrete terms, we plan on developing customized sparse rulers for the lossless recovery of the covariance matrix of different signal types. Furthermore, we will theoretically analyze the number of samples necessary for estimating a covariance matrix under different error guarantees. Finding a minimal sparse ruler can only be accomplished by exhaustive search. To tackle this problem and to attain real-time capability, we will develop smart search heuristics. Moreover, we intend to improve upon known detectors by finding new test statistics. Since better estimation of test statistic parameters leads to improved detection performance, a large part of the cooperative effort will be placed on this topic. Error bounds for the estimated parameters of the test statistics based on the signal covariance matrix will be derived. This will lead to more effective test statistics. As a final step, we will implement the new methods on a software defined radio testbed in order to evaluate their performance in the real world.

可用的无线电频谱已经成为稀缺资源，尽管事实上大部分的许可频谱带未被充分利用。在认知无线电的框架下，人们提出了更有效地利用可用频谱的方法，其核心思想是允许非授权无线电在不干扰授权使用的前提下接入空闲频谱资源。能够可靠地检测授权用户并因此安全地二次利用的方法被称为频谱感测。当前的问题是在非常低的信噪比（SNR）状态下检测信号。已经提出了多种方法，其中一些方法利用通信信号中固有随机特征的存在，例如，信号协方差矩阵的性质。然而，为了可靠地检测通信信号的特征随机特征，必须处理大量的测量数据，为此，我们的目标是开发从大幅减少的样本数量中检测许可发射机的方法和算法。应分析不同类型的协方差估计的误差性能，并开发新的检测器。特别是，我们将作出严格的数学分析，所需的最小数量的样本进行准确的协方差估计在其结构的现实假设。通常，协方差矩阵的估计和二元假设检验（信道空闲或占用）的检验统计量的选择是独立处理的。通过互锁的估计和检测方法和相关的测试统计的新的见解是预期由于预期的cooperation.In具体条款，我们计划开发定制的稀疏规则的无损恢复的协方差矩阵的不同信号类型。此外，我们将从理论上分析在不同的误差保证下估计协方差矩阵所需的样本数量。找到一个最小的稀疏规则只能通过穷举搜索来完成。为了解决这个问题并获得实时能力，我们将开发智能搜索引擎。此外，我们打算通过寻找新的测试统计量来改进已知的检测器。由于更好地估计测试统计参数，导致提高检测性能，很大一部分的合作努力将放在这个主题上。将推导出基于信号协方差矩阵的检验统计量的估计参数的误差界。这将导致更有效的测试统计。作为最后一步，我们将实现一个软件定义的无线电测试平台，以评估其在真实的世界中的性能的新方法。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

SNR walls in eigenvalue-based spectrum sensing

DOI：
10.1186/s13638-017-0899-y
发表时间：
2017-06-19
期刊：
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
影响因子：
2.6
作者：
Bollig, Andreas;Disch, Constantin;Mathar, Rudolf
通讯作者：
Mathar, Rudolf

Compressive cyclostationary spectrum sensing with a constant false alarm rate

DOI：
10.1186/s13638-017-0920-5
发表时间：
2016-10
期刊：
EURASIP Journal on Wireless Communications and Networking
影响因子：
2.6
作者：
Andreas Bollig;A. Lavrenko;Martijn Arts;R. Mathar
通讯作者：
Andreas Bollig;A. Lavrenko;Martijn Arts;R. Mathar

Exact quickest spectrum sensing algorithms for eigenvalue-based change detection

用于基于特征值的变化检测的精确最快的频谱传感算法

DOI：
10.1109/icufn.2016.7537024
发表时间：
2016
期刊：
2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN)
影响因子：
0
作者：
Martijn Arts;Andreas Bollig;Rudolf Mathar
通讯作者：
Rudolf Mathar

Performance limits of cooperative eigenvalue-based spectrum sensing under noise calibration uncertainty

噪声校准不确定性下基于协作特征值的频谱感知的性能限制

DOI：
10.1109/icufn.2016.7537025
发表时间：
2016
期刊：
2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN)
影响因子：
0
作者：
Martijn Arts;Rudolf Mathar
通讯作者：
Rudolf Mathar

Analytical test statistic distributions of the MMME eigenvalue-based detector for spectrum sensing