联合小波-频域变换的自适应能量检测
2024,46(5):90-98
何继爱
兰州理工大学 计算机与通信学院, 甘肃 兰州 730050，hejiai@lut.cn,1037472663@qq.com
李志鑫
兰州理工大学 计算机与通信学院, 甘肃 兰州 730050，hejiai@lut.cn,1037472663@qq.com
王婵飞
兰州理工大学 计算机与通信学院, 甘肃 兰州 730050
张晓霖
兰州理工大学 计算机与通信学院, 甘肃 兰州 730050
兰州理工大学 计算机与通信学院, 甘肃 兰州 730050，hejiai@lut.cn,1037472663@qq.com
李志鑫
兰州理工大学 计算机与通信学院, 甘肃 兰州 730050，hejiai@lut.cn,1037472663@qq.com
王婵飞
兰州理工大学 计算机与通信学院, 甘肃 兰州 730050
张晓霖
兰州理工大学 计算机与通信学院, 甘肃 兰州 730050
摘要：
针对传统能量检测方法在频谱感知领域中极易受低信噪比环境干扰,忽视可用频谱的定位亦会影响频谱状态的判别结果,提出了一种联合小波-频域变换的自适应能量检测方法,旨在提高能量检测的噪声灵敏度和判别精确度。通过离散小波包变换对信号进行分解并计算子带能量；结合能量范数降低自适应阈值的计算复杂度,以便与子带能量比较；采用快速傅里叶变换定位可用频谱范围。对该方法进行模拟仿真,探究自适应阈值与不同性能参数之间的变化关系。仿真结果表明,该方法具有良好的环境适配性与系统稳定性,且在不同信噪比环境下的检测误差更小。此外,对子带信号进行频域分析以实现归一化频率范围的重新排序,进一步提高了频谱感知的准确度。
针对传统能量检测方法在频谱感知领域中极易受低信噪比环境干扰,忽视可用频谱的定位亦会影响频谱状态的判别结果,提出了一种联合小波-频域变换的自适应能量检测方法,旨在提高能量检测的噪声灵敏度和判别精确度。通过离散小波包变换对信号进行分解并计算子带能量；结合能量范数降低自适应阈值的计算复杂度,以便与子带能量比较；采用快速傅里叶变换定位可用频谱范围。对该方法进行模拟仿真,探究自适应阈值与不同性能参数之间的变化关系。仿真结果表明,该方法具有良好的环境适配性与系统稳定性,且在不同信噪比环境下的检测误差更小。此外,对子带信号进行频域分析以实现归一化频率范围的重新排序,进一步提高了频谱感知的准确度。
基金项目：
国家自然科学基金资助项目(61561031,62061024)
国家自然科学基金资助项目(61561031,62061024)
Adaptive energy detection with joint wavelet-frequency domain transform
HE Jiai
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China,hejiai@lut.cn,1037472663@qq.com
LI Zhixin
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China,hejiai@lut.cn,1037472663@qq.com
WANG Chanfei
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
ZHANG Xiaolin
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China,hejiai@lut.cn,1037472663@qq.com
LI Zhixin
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China,hejiai@lut.cn,1037472663@qq.com
WANG Chanfei
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
ZHANG Xiaolin
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
Abstract：
Traditional energy detection method is susceptible to the interference of low SNR(signal-to-noise ratio) environment in the field of spectrum sensing, and neglecting the localization of available spectrum could also affect the discriminative results of spectrum states. In order to improve the noise sensitivity and discrimination accuracy of energy detection, an adaptive energy detection method by combining wavelet-frequency domain transform was proposed. Signal was decomposed by discrete wavelet packet transform to calculate the sub-band energy; the computational complexity of the adaptive threshold was reduced by combining the norm of energy so as to facilitate comparison with the sub-band energy; the available spectrum was located by fast Fourier transform. And the method was simulated to explore the variable relationship between the adaptive threshold and different performance parameters. Simulation results show that the method has good environmental adaptability and system stability, while the detection error is smaller in different SNR environments. In addition, the frequency domain analysis of the sub-band signal to achieve the reordering of the normalized frequency range, which further improves the accuracy of spectrum sensing.
Traditional energy detection method is susceptible to the interference of low SNR(signal-to-noise ratio) environment in the field of spectrum sensing, and neglecting the localization of available spectrum could also affect the discriminative results of spectrum states. In order to improve the noise sensitivity and discrimination accuracy of energy detection, an adaptive energy detection method by combining wavelet-frequency domain transform was proposed. Signal was decomposed by discrete wavelet packet transform to calculate the sub-band energy; the computational complexity of the adaptive threshold was reduced by combining the norm of energy so as to facilitate comparison with the sub-band energy; the available spectrum was located by fast Fourier transform. And the method was simulated to explore the variable relationship between the adaptive threshold and different performance parameters. Simulation results show that the method has good environmental adaptability and system stability, while the detection error is smaller in different SNR environments. In addition, the frequency domain analysis of the sub-band signal to achieve the reordering of the normalized frequency range, which further improves the accuracy of spectrum sensing.
收稿日期：
2022-04-27
2022-04-27
