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Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This paper aims to study the synergistic effect of graphene sponge on the thermal properties and shape stability of composite phase change material (PCM).

Graphene oxide sponge is first prepared from an aqueous solution of graphene oxide by freeze-drying method. The oxidized graphene sponge is reduced by hydrazine hydrate. Finally, use vacuum impregnation method to introduce paraffin into graphene sponge to prepare composite PCM.

Graphene sponge is used to improve the shape stability of paraffin wax and improves the thermal conductivity and latent heat of the composite PCM. The thermal conductivity increases by 200 per cent and the composite PCM has excellent reliability in 100 melt-freezing cycles.

A simple way for fabricating composite PCM with high thermal conductivity and latent heat which has the potential to be used as thermal storage materials without container encapsulation has been developed by using graphene sponge and paraffin.

The materials and preparation methods with special structure and properties in this paper provide a new idea for the research of PCM, which can be widely used in the fields of energy conversion and storage.