欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为了解决认知无线电中能量检测法在低信噪比下检测概率低的问题,本文提出了一种基于SVD分解的频谱感知算法。首先利用接收信号构造Hankel矩阵,通过SVD分解,将矩阵分离成信号空间与噪声空间,再将较小的奇异值置零,然后重构矩阵,从而提高接收信号的信噪比（SNR）。其次,将SVD系统输出信号功率对噪声功率进行归一化,把降低噪声功率转化成提高主用户信号功率。最后进行能量检测,以此来提高检测概率。理论分析和计算机仿真表明,在相同条件下,基于SVD分解的频谱感知算法与传统的能量检测法相比,检测概率显著提高;要达到相同的检测概率,对信噪比的要求也显著降低。
英文摘要：
In order to solve the problem that detection probability based on energy detector in cognitive radio is low under low Signal-to-Noise Ratio（SNR）conditions,this paper proposes a novel spectrum-sensing method for cognitive radio based on singular value decomposition（SVD）.First,the received signal is represented in Hankel matrix,which is divided into signal space and noise space by using SVD.By ignoring the smaller singular values and then reconstructing the matrix,the SNR of received signal is increased.Then the power of the output signal of SVD system is normalized by noise power,and thus the reduction of noise power amounts to the improvement of user signal power.Finally,energy-detection is employed to the normalized signal,through which the detection probability is improved.Theoretical analyses and computer simulation results both shows that the detection probability of the proposed spectrumsensing technique based on singular value decomposition is better than traditional energy-detection under the same circumstances.Besides, to achieve the same detection probability,the proposed method requires lower SNR.
同期刊论文项目
同项目期刊论文
A topology preserving non-rigid registration algorithm with integration shape knowledge to segment b
A computer-aided diagnostic system to discriminate SPIO-enhanced magnetic resonance hepatocellular c
Heavy-tailed distribution and local long distribution and local long memory in time series molecular
An automatic method of brain tumor segmentation from MRI volume based on the symmetry of brain and l
The equivalency of minimum error entropy criterion and minimum dispersion criterion for symmetric st
Noise and disturbance reduction for heart sounds in the cycle frequency domain Based on non-linear t
期刊信息
位置：