摘要
在现代无线通信环境下,由于噪声环境复杂多变,且信号在不同感知周期内的占空比变化较大,导致信号频谱感知能力的下降,甚至造成非授权用户对授权用户的干扰。针对此问题,提出一种基于峰度值估计的智能无线信号频谱感知方法。以典型的非高斯噪声分布(McLeish分布)作为通用背景噪声,依据多尺寸跳跃连接的思想构建深度神经网络框架,结合注意力机制捕获目标信号的多尺寸特征,在感知周期内占空比不确定的条件下,完成对目标信号峰度值的估计,通过对估计值的判决,实现在不同噪声模型下对无线信号的感知。仿真结果表明:在信噪比SNR≥-10 dB条件下,当虚警概率P_(f)=0.02,感知占空比0.5≤η<1时,其平均检测概率达到了84.3%以上;当噪声功率估计误差ε≤2、P_(f)=0.01时,其平均检测概率达到96.1%以上。证明所提方法具有较强的抗占空比和噪声功率不确定性的能力,具备一定的理论研究意义和工程实用价值。
The complexity and variability of noise in modern wireless radio communication environment and the great diversity of the duty cycles of signals in different sensing periods cause the decline in the sensing ability of signal spectrums,even lead to interference to authorized users by unauthorized users.An intelligent wireless signal spectra sensing method based on the estimation of kurtosis is proposed to solve the above problems.A deep neural network framework is constructed based on the idea of multi-scale skip connections,which usestypical non-Gaussian noise distribution(McLeish distribution)as the general background noise.The multi-scale features of target signal are captured by means of the attention mechanism.The kurtosis value of target signal is estimated under the condition of uncertain duty cycle in the sensing period.The wireless signals under different noise models are sensed by judging the estimated value.The simulated results indicate that the average detection probability reaches over 84.3%when P_(f)=0.02 and duty cycle 0.5≤η<1 under the condition of SNR≥-10 dB.And it reaches over 96.1%when noise power estimation errorε≤2 and P_(f)=0.01.It is proven that the proposed method has strong resistance to duty cycle and noise power uncertainty,and has certain theoretical research significance and engineering practical value.
作者
王洋
冯永新
钱博
宋碧雪
WANG Yang;FENG Yongxin;QIAN Bo;SONG Bixue(Key Laboratory of Information Network and Information Countermeasure Technology of Liaoning Province,Shenyang Ligong University,Shenyang 110159,Liaoning,China)
出处
《兵工学报》
北大核心
2025年第7期305-316,共12页
Acta Armamentarii
基金
国家自然科学基金项目(61971291)
中央引导地方科技发展项目(2022020128-JH6/1001)
沈阳市自然科学基金项目(22-315-6-10)。
