电讯技术2025，Vol.65Issue(8)：1213-1220,8.DOI:10.20079/j.issn.1001-893x.240421001
基于多任务学习的通用滤波多载波调制识别与信噪比估计
UFMC Modulation Recognition and SNR Estimation Based on Multi-task Learning
摘要
Abstract
The modulation recognition and signal-to-noise ratio(SNR)estimation problems of the subcarriers of the universal filter multi-carrier(UFMC)signal in non-cooperative communication need to be solved,but the current research only focuses on a single task.Therefore,a neural network model using a multi-task learning framework is proposed to solve the modulation recognition and SNR estimation tasks at the same time.Firstly,the receiver signal of the UFMC system is obtained,and the orthogonal component of the signal is solved as the input feature.Then,a neural network is constructed on the multi-task learning framework.The neural network adopted is a convolution neural network and a long short-term memory network in series.Finally,the above model is used to solve the two tasks jointly.Experimental results show that the performance of the multi-task learning model constructed is better than that of single-task learning.When the SNR is 0 dB,the accuracy of subcarrier modulation recognition is improved by 7.71%,and the mean square error of SNR estimation is reduced by 45.6%.关键词
通用滤波多载波(UFMC)/调制识别/信噪比估计/多任务学习/神经网络Key words
universal filtered multi-carrier(UFMC)/modulation identification/SNR estimation/multi-task learning/neural network分类
信息技术与安全科学引用本文复制引用
张天骐,吴云戈,吴仙越,李春运..基于多任务学习的通用滤波多载波调制识别与信噪比估计[J].电讯技术,2025,65(8):1213-1220,8.基金项目
重庆市自然科学基金项目(cstc2021jcyj-msxmX0836) （cstc2021jcyj-msxmX0836）
