频谱感知技术是认知无线电的核心技术之一,由于未来无线通信技术的发展对高速数据通信的需求,使得宽带频谱感知技术成为目前研究的重点方向。由于宽频带带宽较宽不能直接将整个频段划分为占用或者空闲,需要对宽频带进行细分。将信号划...频谱感知技术是认知无线电的核心技术之一,由于未来无线通信技术的发展对高速数据通信的需求,使得宽带频谱感知技术成为目前研究的重点方向。由于宽频带带宽较宽不能直接将整个频段划分为占用或者空闲,需要对宽频带进行细分。将信号划分为多个子频带,通过预处理将多元分类问题转化成二元分类问题。为了降低频谱感知的算法复杂度,提出了基于噪声估计(estimation of noise,EN)和支持向量机(support vector machine,SVM)的频谱感知算法,该算法利用检测性能较好的慢速感知算法作为噪声估计,再使用算法复杂度低的快速感知算法结合噪声估计的信息进行频谱感知。实验结果表明,在低信噪比下,该算法较传统的方法其检测性能有着明显的提高,在信噪比为-10dB的无线环境中能够完全识别各个子信道的使用情况。展开更多
Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a prior...Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a priori. This paper presents a cooperative sensing scheme based on se-quential compressed sensing where sequential measurements are collected from the analog-to-information converters. A novel cooperative compressed sensing recovery algorithm named Simul-taneous Sparsity Adaptive Matching Pursuit (SSAMP) is utilized for sequential compressed sensing in order to estimate the reconstruction errors and determinate the minimal number of required meas-urements. Once the fusion center obtains enough measurements, the reconstruction spectrum sparse vectors are then used to make a decision on spectrum occupancy. Simulations corroborate the effec-tiveness of the estimation and sensing performance of our cooperative scheme. Meanwhile, the per-formance of SSAMP and Simultaneous Orthogonal Matching Pursuit (SOMP) is evaluated by Mean-Square estimation Errors (MSE) and sensing time.展开更多
文摘频谱感知技术是认知无线电的核心技术之一,由于未来无线通信技术的发展对高速数据通信的需求,使得宽带频谱感知技术成为目前研究的重点方向。由于宽频带带宽较宽不能直接将整个频段划分为占用或者空闲,需要对宽频带进行细分。将信号划分为多个子频带,通过预处理将多元分类问题转化成二元分类问题。为了降低频谱感知的算法复杂度,提出了基于噪声估计(estimation of noise,EN)和支持向量机(support vector machine,SVM)的频谱感知算法,该算法利用检测性能较好的慢速感知算法作为噪声估计,再使用算法复杂度低的快速感知算法结合噪声估计的信息进行频谱感知。实验结果表明,在低信噪比下,该算法较传统的方法其检测性能有着明显的提高,在信噪比为-10dB的无线环境中能够完全识别各个子信道的使用情况。
基金Supported by the National High Technology Research and Development Program(No.2009AA01Z241)the National Natural Science Foundation(No.60971129,No.61071092)
文摘Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a priori. This paper presents a cooperative sensing scheme based on se-quential compressed sensing where sequential measurements are collected from the analog-to-information converters. A novel cooperative compressed sensing recovery algorithm named Simul-taneous Sparsity Adaptive Matching Pursuit (SSAMP) is utilized for sequential compressed sensing in order to estimate the reconstruction errors and determinate the minimal number of required meas-urements. Once the fusion center obtains enough measurements, the reconstruction spectrum sparse vectors are then used to make a decision on spectrum occupancy. Simulations corroborate the effec-tiveness of the estimation and sensing performance of our cooperative scheme. Meanwhile, the per-formance of SSAMP and Simultaneous Orthogonal Matching Pursuit (SOMP) is evaluated by Mean-Square estimation Errors (MSE) and sensing time.
