Blind multicarrier waveform recognition has become a more daunting task and open problem for the current and future radio surveillance and signals interception, with the advent of new multicarrier technologies such as the state-of-the-art F-OFDM, UFMC, FBMC, OTFS, GFDM and CP-OFDM techniques. Therefore, the practical recognition scheme for multicarrier waveforms is necessary to keep up with the pace. To tackle this challenge, we propose a novel multicarrier waveform recognition framework based on Spatial Temporal-Convolutional Long Short-Term Deep Neural Network (ST-CLDNN) in the entirely blind context. The complementary information of the raw in-phase, quadrature and amplitude samples are first extracted to provide more distinguishable features. Then ST-CLDNN collects the advantages of one-dimensional convolutional and long short-term memory (LSTM) to extract high-level spatial and temporal features for the recognition task. Later, we introduce the transfer learning strategy to put the computational resource to good use and obviate the retraining from scratch for a time-varying channel. Experimental results indicate that the proposed ST-CLDNN can perform better than the traditional feature-based classifiers and existing deep learning (DL)-based neural networks, and deliver a substantial recognition performance in a time-varying multipath fading channel.

随着最新的F-OFDM，UFMC，FBMC，OTFS等新的多载波技术的出现，盲目多载波波形识别已成为当前和未来无线电监视和信号拦截的更艰巨的任务和未解决的问题。 ，GFDM和CP-OFDM技术。因此，必须跟上多载波波形的实际识别方案。为了解决这一挑战，我们提出了一种在空盲环境下基于时空卷积长短期深层神经网络（ST-CLDNN）的新颖的多载波波形识别框架。首先提取原始同相，正交和幅度样本的补充信息，以提供更多可区分的特征。然后，ST-CLDNN收集一维卷积和长短期记忆（LSTM）的优势，以提取用于识别任务的高级时空特征。稍后，我们将介绍转移学习策略，以充分利用计算资源，并避免时变通道从头开始进行重新训练。实验结果表明，所提出的ST-CLDNN的性能优于传统的基于特征的分类器和现有的基于深度学习（DL）的神经网络，并在时变多径衰落信道中提供了可观的识别性能。我们介绍了转移学习策略，以充分利用计算资源，并避免时变通道从头开始进行重新训练。实验结果表明，所提出的ST-CLDNN的性能优于传统的基于特征的分类器和现有的基于深度学习（DL）的神经网络，并在时变多径衰落信道中提供了可观的识别性能。我们介绍了转移学习策略，以充分利用计算资源，并避免时变通道从头开始进行重新训练。实验结果表明，所提出的ST-CLDNN的性能优于传统的基于特征的分类器和现有的基于深度学习（DL）的神经网络，并在时变多径衰落信道中提供了可观的识别性能。