摘要
为提升调制识别的准确性和鲁棒性,本文提出了一种全新的双模态混合调制识别模型.模型同时考虑原始时域同相正交(in-phase and quadrature,I/Q)和幅度相位(amplitude and phase,A/P)双模态数据以探索信号的时空相关性.采用双路对称结构对A/P模态数据进一步处理,更有效地学习数据间的重复特征,避免信息冗余.模型中引入双向长短时记忆网络(bidirectional long short-term memory network,BiLSTM),利用其双向时序特征提取能力,增强模型对复杂时序信息的理解.实验结果表明,所提模型在数据集RadioML2016.10A上表现良好.当SNR低于−8 dB时,平均识别精度比主流模型提升6%,而SNR在0–18 dB时,平均识别精度比主流模型提高2%–10%,且在SNR为16 dB时,识别精度高达94.32%.另外,将模型迁移到数据集RadioML2016.10B所得结果同样最优,且当SNR为18 dB时识别精度高达93.91%.
To improve the accuracy and robustness of modulation recognition,this study proposes an improved bimodal hybrid modulation recognition model.The model incorporates both the original time-domain in-phase and quadrature(I/Q)data,as well as amplitude and phase(A/P)format data to explore the spatiotemporal correlations within the signal.A two-branch symmetric structure is applied to further process the A/P data,enabling more effective learning of repetitive patterns while mitigating information redundancy.A bidirectional long short-term memory(BiLSTM)network is introduced to enhance the model’s capacity for complex temporal feature extraction.Experimental results demonstrate that the proposed model performs well on the RadioML2016.10A dataset.When the signal-to-noise ratio(SNR)is below–8 dB,the average recognition accuracy surpasses mainstream models by 6%.Within the SNR range of 0 to 18 dB,the average recognition accuracy improves by 2%to 10%,reaching 94.32%at 16 dB.In addition,when applied to the RadioML2016.10B dataset,the model continues to achieve superior performance,attaining a recognition accuracy of 93.91%at 18 dB.
作者
郭业才
王孟杰
毛湘南
胡晓伟
GUO Ye-Cai;WANG Meng-Jie;MAO Xiang-Nan;HU Xiao-Wei(School of Electronics&Information Engineer,Nanjing University of Information Science&Technology,Nanjing 210044,China;Tianchang Research Institute,Nanjing University of Information Science&Technology,Chuzhou 239399,China;School of Electronics and Information Engineer,Wuxi University,Wuxi 214105,China)
出处
《计算机系统应用》
2025年第8期169-178,共10页
Computer Systems & Applications
关键词
自动调制识别
深度学习
双路对称
双向提取特征
automatic modulation recognition
deep learning
two-way symmetric
bidirectional extract feature
