基于小波能量比和改进阈值函数的通信信号降噪算法

doi:10.12305/j.issn.1001-506X.2025.07.03

摘要/Abstract

摘要：

为了提高低信噪比下采样率受限且中心频率未知的通信信号质量, 提高识别性能, 本文实现中心频率的自适应估计并提出一种改进的小波降噪算法。中心频率估计部分利用11类通信信号在功率谱上的差异实现粗分类, 基于不同分类结果对频率居中法进行改进以实现中心频率的估计。改进的小波降噪算法一方面针对软硬阈值函数存在的问题提出一种参数可调且连续的改进小波阈值函数; 另一方面采用小波能量比刻画不同中心频率的通信信号小波系数能量分布, 根据大小对小波系数采取不同的处理方法。最后, 针对11类通信信号, 在[-10, 10] dB的信噪比范围内进行调制识别实验。仿真结果表明, 所提降噪算法对11类通信信号都有显著的降噪效果, 在[-10, 0] dB的信噪比范围内相较于未降噪时的信号平均识别率提升了10%~40%。

关键词: 低信噪比, 未知中心频率, 改进小波阈值, 小波能量比, 调制识别

Abstract:

To improve the quality of communication signals with low signal-to-noise ratio (SNR), limited sampling rates, and unknown center frequencies, and to enhance recognition performance, this paper implements adaptive estimation of the center frequency and proposes an improved wavelet denoising algorithm. The central frequency estimation section realizes rough classification based on the differences in the power spectrum of 11 types of communication signals, and improves the frequency centering method based on different classification results to achieve the estimation of the central frequency. The improved wavelet denoising algorithm addresses issues with soft and hard threshold functions by proposing a parameter-adjustable and continuous wavelet threshold function. Additionally, it uses wavelet energy ratios to characterize the energy distribution of wavelet coefficients for communication signals with different center frequencies, applying different processing methods based on their magnitudes. Finally, modulation recognition experiments are conducted on 11 types of communication signals within an SNR range of [-10, 10] dB. Simulation results show that the proposed denoising algorithm achieves notable noise reduction for all 11 types of communication signals, exhibiting an improvement of 10%-40% in the average signal recognition rate within the SNR range of [-10, 0] dB compared to the unprocessed signals.

Key words: low signal-to-noise ratio(SNR), unknown center frequency, improved wavelet threshold, wavelet energy ratio, modulation recognition

中图分类号:

TN911.3

连家威, 张晓林, 颜品, 孙溶辰. 基于小波能量比和改进阈值函数的通信信号降噪算法[J]. 系统工程与电子技术, 2025, 47(7): 2098-2109.

Jiawei LIAN, Xiaolin ZHANG, Pin YAN, Rongchen SUN. Communication signal denoising algorithm based on wavelet energy ratio and improved threshold function[J]. Systems Engineering and Electronics, 2025, 47(7): 2098-2109.

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参数	参数取值/类型	参数	参数取值/类型
采样频率/Hz	16 384	信噪比范围/dB	[-10, 10]
中心频率/Hz	2 000	阶数	4
码元速率/B	128	阈值类型	Sqtwolog阈值
码元个数	256	阈值函数	硬阈值

小波基	平均SNR/dB	平均RMSE
db4	5.84	0.43
sym4	5.85	0.43
coif4	6.66	0.41

参数	参数取值/类型	参数	参数取值/类型
采样频率/Hz	16 384	信噪比范围/dB	[-10, 10]
码元个数	256	阈值类型	式(12)阈值
码元速率/B	128	分解层数	4
中心频率/Hz	4 000	蒙特卡罗仿真次数	500

处理方法	f_c=1 100 Hz				f_c=1 700 Hz
处理方法	[-10, -5]	[-4, 0]	^{[1, 5]}	^{[6, 10]}	[-10, -5]	[-4, 0]	^{[1, 5]}	^{[6, 10]}
处理1	2.96/0.70	2.93/0.37	2.87/0.20	2.79/0.11	3.58/0.82	3.54/0.43	3.45/0.23	3.21/0.12
处理2	7.34/1.39	5.23/0.58	3.54/0.24	2.68/0.11	8.09/1.47	6.42/0.67	3.51/0.24	-0.51/-0.01
处理3	6.12/1.22	5.59/0.61	4.97/0.31	4.39/0.16	6.03/1.25	6.00/0.64	5.15/0.32	3.43/0.13
本文方法	7.71/1.42	6.87/0.70	5.92/0.36	4.87/0.17	8.09/1.47	6.42/0.67	5.32/0.33	3.40/0.13

信号种类	T₁	T₂	T₃
2ASK	256	-	-
4ASK	69.222 4	-	-
8ASK	76.733 6	-	-
4PSK	0	4	-
8PSK	0	0	1
2FSK	0	0	0
4FSK	0	0	0
8FSK	0	0	0
16QAM	0	9.618	196
32QAM	0	0.076	4
64QAM	0	0.692	132.25