To improve the recognition rate of the end-to-end modulation recognition method based on deep learning, a modulation recognition method of communication signals based on a cascade network is proposed, which is composed of two networks: Stacked Denoising Auto Encoder (SDAE) network and DCELDNN (Dilated Convolution, ECA Mechanism, Long Short-Term Memory, Deep Neural Networks) network. SDAE network is used to denoise the data, reconstruct the input data through encoding and decoding, and extract deep information from the data. DCELDNN network is constructed based on the CLDNN (Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks) network. In the DCELDNN network, dilated convolution is used instead of normal convolution to enlarge the receptive field and extract signal features, the Efficient Channel Attention (ECA) mechanism is introduced to enhance the expression ability of the features, the feature vector information is integrated by a Global Average Pooling (GAP) layer, and signal features are extracted by the DCELDNN network efficiently. Finally, end-to-end classification recognition of communication signals is realized. The test results on the RadioML2018.01a dataset show that the average recognition accuracy of the proposed method reaches 63.1% at SNR of -10 to 15 dB, compared with CNN, LSTM, and CLDNN models, the recognition accuracy is improved by 25.8%, 12.3%, and 4.8% respectively at 10 dB SNR.

Automatic modulation recognition(AMR) of radar signals is a currently active area, especially in electronic reconnaissance, where systems need to quickly identify the intercepted signal and formulate corresponding interference measures on computationally limited platforms. However, previous methods generally have high computational complexity and considerable network parameters, making the system unable to detect the signal timely in resource-constrained environments. This letter firstly proposes an efficient modulation recognition network(EMRNet) with tiny and low latency models to match the requirements for mobile reconnaissance equipments. One-dimensional residual depthwise separable convolutions block(1D-RDSB) with an adaptive size of receptive fields is developed in EMRNet to replace the traditional convolution block. With 1D-RDSB, EMRNet achieves a high classification accuracy and dramatically reduces computation cost and network paraments. The experiment results show that EMRNet can achieve higher precision than existing 2D-CNN methods, while the computational cost and parament amount of EMRNet are reduced by about 13.93× and 80.88×, respectively.

Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.

The recognition vector of the decision-theoretic approach and that of cumulant-based classification are combined to compose a higher dimension hyperspace to get the benefits of both methods. The method proposed in this paper can cover more kinds of signals including signals with order higher than 4 in the AWGN channel even under low SNR values, i.e. those down to -5 dB. The composed vector is input into an RBF neural network to get more reasonable reference points. Eleven kinds of signals, say 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 2FSK 4FSK, 8FSK, 16QAM and 64QAM, are involved in the discussion.

Software-Defined Radio (SDR) receiver has the ability of operating in a multi-mode environment and has wide applications. However, efficient recognition of the currently active modulation system in real-time is a major problem faced by many applications. In this paper, an efficient method for the recognition of modulation system in a SDR receiver is proposed. The method is a classical two-stage approach based on (i) decision feature extraction and (ii) modulation system classification. In the first stage, decision features are extracted by the use of digital quadrature polyphase filter. In the second stage, an efficient parallel decision algorithm is proposed to classify the active modulation type. This proposed algorithm is proof to be more efficient than the conventional type of decision-tree approach. The complete recognition system is implemented using MATLAB. Simulation result shows that the proposed method achieved good robustness even with the presence of band-limited Additive White Gaussian Noise (AWGN). The overall successful recognition rate of 98.5% can be achieved even at a low signal-to-noise ratio (SNR) of 8 dB.