Kurtosis-based Spectrum Sensing Method for Wireless Signals

doi:10.12382/bgxb.2024.0441

Abstract

Abstract:

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≥-10dB.And it reaches over 96.1% when noise power estimation error ε≤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.

Key words: spectrum sensing, convolutional neural network, signal detection, McLeish distribution

CLC Number:

TN97

WANG Yang, FENG Yongxin, QIAN Bo, SONG Bixue. Kurtosis-based Spectrum Sensing Method for Wireless Signals[J]. Acta Armamentarii, 2025, 46(7): 240441-.

Figures/Tables 21

Fig.1 Design of spectra sensing framework for kurtosis estimation

Fig.2 Multidimensional feature fitting network framework

Table 1 Main parameters of target sensing signals

调制信号	过采样率	扩频增益/dB	伪码长度
DSSS	4	18.06	4096
FHSS	4	18.06	4096
QPSK	4

Table 2 Main parameters of training and testing sets

数据集	样本总数	噪声参数	信噪比/dB	样本尺寸
训练集	19.2万	v=1,3,5,∞ σ²=1	-15~0	2×4096
测试集1	4.8万	v=1,3,5,∞ σ²=1,σ²≤2	-15~0	2×4096 2×1024
测试集2	4.8万	v=1,3,5,∞ 0.5≤η<1	-15~0	2×4096

Table 3 Network training parameters

优化器	学习率	权重衰减	训练次数	验证方法	早停策略
Adam	0.001	10^-4	50	十折交叉	是

Table 4 Feature sizes of feature fusion module

模块名称	特征尺寸
模块名称	1×8	1×16	1×32	1×64	1×128
SSFF	2	3
BSFF				2	3
MSFF	1	1	1	1	1

Fig.3 Performance comparison of feature fusion modules with different sizes

Table 5 Comparison of detection probabilities of different feature fusion modules %

模块名称	平均检测概率
模块名称	v→∞ SNR≤-12	v→∞ SNR>-12	v=3 SNR<-13	v=3 SNR≥-13
SSFF	61.64	96.01	72.90	99.59
BSFF	67.77	93.40	81.05	96.75
MSFF	66.44	95.00	79.57	99.49

Fig.4 Comparison of detection probabilities for different signals under different signal-to-noise ratios

Fig.5 Comparison of average detection probabilities under different signal-to-noise ratios

Fig.6 Comparison of average detection probabilities under different false alarm probabilities

Fig.7 Comparison of average detection probabilities under different signal-to-noise ratios

Fig.8 Comparison of average detection probabilities under different false alarm probabilities

Fig.9 Comparison of average detection probabilities under different signal-to-noise ratios

Fig.10 Comparison of average detection probabilities under different false alarm probabilities

Fig.11 Comparison of average detection probabilities under different signal-to-noise ratios

Fig.12 Comparison of average detection probabilities under different false alarm probabilities

Table 6 Main parameters of different numbers of training sets

数据集	样本总数/10⁴	噪声参数	信噪比/dB	样本尺寸
训练集1	1.92	v=∞ σ²=1 v=∞ σ²=1	-15~0	2×4096
训练集2	9.6	v=∞ σ²=1 v=∞ σ²=1	-15~0	2×4096

Fig.13 Comparison of average detection probabilities under different signal-to-noise ratios(training sets 1 and 2)

Fig.14 Comparison of average detection probabilities under different false alarm probabilities(training sets 1 and 2)

Table 7 Comparison of method complexity

名称	参数量	计算量	训练时长/ms	推理时长/ms
本文方法	1.23×10⁶	2.73×10⁸	4491.11	853.33
SSCL	1.07×10⁶	2.09×10⁸	4470.14	847.82
SSDNN	1.11×10⁵	2.98×10⁷	4228.19	823.19

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