认知无线网络中基于压缩感知的自适应非重构宽带频谱感知方法研究

项目名称： 认知无线网络中基于压缩感知的自适应非重构宽带频谱感知方法研究

项目编号： No.61201161

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 曹开田

作者单位： 南京邮电大学

项目金额： 25万元

中文摘要： 宽带频谱感知作为CR技术的关键技术之一，是CR网络技术得以应用的基础和前提。当前，利用CS理论进行宽带频谱感知已成为一个新的研究热点。本项目拟采用CS理论对非重构的宽带频谱感知方法进行研究，研究内容主要包括：在噪声及原信号稀疏度未知的情况下，探索AIC压缩采样速率自适应算法；构建基于压缩观测数据高阶统计量的频谱感知数学模型，克服现有频谱感知方法采用二阶统计量进行感知所带来的感知性能不佳的问题；尝试采用Replica Method理论，对基于AIC观测数据协方差矩阵特征值及熵的宽带频谱感知方法进行研究；针对各认知节点压缩采样的异步性及观测次数的差异性等特点，拟采用Bayesian方法构建直接基于观测数据的异步协作频谱感知方法。通过对以上内容研究，探索出复杂度低、自适应、非重构的宽带频谱压缩感知方法，实现复杂网络环境下对宽带频谱信号进行快速、准确的感知，提高整个CR网络的频谱利用率和系统容量。

中文关键词： 认知无线电；压缩感知；稀疏采样；宽带频谱感知；

英文摘要： Wideband spectrum sensing, as one of the key techniques for cognitive radio(CR), determines whether CR network can be applied to the practical system or not. Recently, compressed sensing(CS) based wideband spectrum sensing for cognitive radio networks is a very hot research topic. This project attempts to research into the non-reconstruction wideband spectrum sensing schemes based on CS, and includes the following main research points. Firstly, the adaptive compressive sampling algorithm for AIC (Analog to Information Converter) is developed without the sparsity and the noise variance as priori knowledge. Secondly, in order to solve the problem that the sensing performance of the existing spectrum sensing methods is not high by the second order statistics, the spectrum sensing model based on the high order statistics of the compressed measurements is studied. Thirdly, by utilizing Replica Method theory, this project plans to explore novel spectrum sensing algorithms based on the statistical characteristics of the entropy and the eigenvalue of compressive sampling covariance matrix. Finally, according to the fact that each secondary user (SU) can not synchronously obtain the compressed measurements and the length of these measurements is not the same, the model of the asynchronous cooperative spectrum sensing bas

英文关键词： Cognitive radios；compressed sensing；Sparsely sampling；Wideband spectrum sensing；