一种采用压缩协方差的无线电宽带频谱感知方法

A Radio Wideband Spectrum Sensing Method with Compressed Covariance

为有效降低宽带频谱感知的观测时间和计算复杂度,提出了一种基于压缩协方差的无线电宽带频谱感知方法。首先,通过循环稀疏规则测量不同标记的距离,运用多陪集采样组代替奈奎斯特模数转换器,形成基于多陪集采样库的欠奈奎斯特采样结构;其次,构建压缩协方差频谱感知模型,运用频谱决策模块对输入样本进行处理,完成频谱分析;最后,通过Matlab生成测试信号数据,对所提频谱感知算法进行建模与性能分析。实验结果表明,所提方法能够将检测误差控制在有效范围内,且与传统频谱检测方法相比,所提方法在不同信噪比环境下具有更高的频谱检测水平度。

To reduce the observation time and computational complexity of wideband spectrum sensing,a radio wideband spectrum sensing method based on compressed covariance is proposed.First,the distance of different markers is measured by the circular sparse rule,and the multi-coset sampling group is used to replace the Nyquist analog-to-digital converter to form an under-Nyquist sampling structure based on the multi-coset sampling library.Secondly,the compressed covariance spectrum sensing model is constructed,and the input samples are processed by the spectrum decision module to complete the spectrum analysis.Finally,the test signal data is generated by Matlab to model and analyze the performance of the proposed spectrum sensing algorithm.The experimental results show that the proposed method can control the detection error within the effective range,and compared with the traditional spectrum detection method,the proposed method can greatly reduce the hardware power consumption and storage space,and effectively reduce the computational complexity.