communication signal
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In the noncooperation communication scenario, digital signal modulation recognition will help people to identify the communication targets and have better management over them. To solve problems such as high complexity, low accuracy and cumbersome manual extraction of features by traditional machine learning algorithms, a kind of communication signal modulation recognition model based on convolution neural network (CNN) is proposed. In this paper, a convolution neural network combines bidirectional long short-term memory (BiLSTM) with a symmetrical structure to successively extract the frequency domain features and timing features of signals and then assigns importance weights based on the attention mechanism to complete the recognition task. Seven typical digital modulation schemes including 2ASK, 4ASK, 4FSK, BPSK, QPSK, 8PSK and 64QAM are used in the simulation test, and the results show that, compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR, which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.

In order to solve the problem of large signal acquisition error caused by radio wave multipath effect in indoor environment, firstly, the signal source carried on the motion platform is collected for spectrum signal, and the signal processed by wavelet threshold denoising algorithms extracted and stored for spectrum feature extraction. Then, after data training and identification, the signal source is input into the system in random mode for identification. The experimental results show that the improved fuzzy clustering algorithm (FCA) is 12.7% higher than the spectrum envelope extraction method (SEEM) in the recognition rate of spectrum characteristics of different modes of signal source.

Human sweat odor serves as social communication signal for a person’s traits and emotional states. This study explored whether body odors can also communicate information about one’s self-esteem, and the role of applied fragrance in this relationship. Female participants were asked to rate self-esteem and attractiveness of different male contestants of a dating show, while being exposed to male participant’s body odors differing in self-esteem. High self-esteem sweat was rated more pleasant and less intense than low self-esteem sweat. However, there was no difference in perceived self-esteem and attractiveness of male contestants in videos, hence explicit differences in body odor did not transfer to judgments of related person characteristics. When the body odor was fragranced using a fragranced body spray, male contestants were rated as having higher self-esteem and being more attractive. The finding that body odors from male participants differing in self-esteem are rated differently and can be discriminated suggests self-esteem has distinct perceivable olfactory features, but the remaining findings imply that only fragrance affect the psychological impression someone makes. These findings are discussed in the context of the role of body odor and fragrance in human perception and social communication.

Abstract Carbon nanotube (CNT) field-emission X-ray source has great potential in X-ray communication (XCOM) because of its controllable emission and instantaneous response. A novel voltage loading mode was proposed in this work to achieve high-frequency pulse X ray-emission. The characteristics of cathode current and pulse X-ray versus voltage, frequency, and pulse amplitude were studied, and XCOM data transmission experiment was carried out. Results showed that the CNT cold cathode X-ray source, as a communication signal source, could work in 1.05 MHz pulse emission frequency. When the grid voltage was higher than 470 V, the pulse X-ray waveform amplitude achieved peak, and the shape exhibited a pseudo square wave. The duty cycle of the X-ray waveform exceeded 50%, reaching 56% when the pulse frequency reached 1 MHz. In the XCOM data transmission experiment, the pulsed X-ray waveform was well consistent with the loading data signal voltage waveform under different pulse-emission frequencies. This work realized the X-ray high-frequency pulse emission of CNT cold cathode X-ray source and lays a foundation for the development and application of CNT cold cathode X-ray source in XCOM.

Abstract In the process of communication, modulation signal recognition and classification are an important part of non-cooperative communication. Automatic modulation recognition technology of communication signals based on feature extraction and pattern recognition is a key research object in the radio field. The use of neural network can achieve automatic recognition of a variety of modulation signals and achieve good results. In this method, the received signal is preprocessed to obtain the complex baseband signal including in-phase component and orthogonal component. As the data set of the input convolution neural network model, the signal further optimizes the traditional method of manual extraction of expert features for communication signal recognition, which has great limitations and low accuracy under low signal-to-noise ratio, and the simulation results are verified. The results show that the proposed method has stronger feature representation ability and competitiveness in automatic modulation recognition, and is helpful to promote the application of deep learning in the field of automatic modulation recognition.

Abstract With the rapid development of the information age, mobile communication networks have gradually entered people’s lives. The emergence of 5G networks has accelerated the convenience of people’s communication and contact, and has become an advanced information technology that has been widely used in various fields. Although the emergence of 5G networks is beneficial, the remote transmission quality of communication signals has also caused many problems. The optimization of remote transmission quality can effectively improve the operational stability of the communication network. There are many ways to optimize the transmission quality of communication signals. For example, when signal interference needs to be reduced, calculate the average value of signals in various places, set the interference reduction threshold function, and complete the remote transmission quality optimization of communication signals. The existing method is to use microblogs with hard and soft thresholds to decompose the signal quality through multiple scales to provide interference reduction thresholds, but ignore the calculation of the average value of the subband signals, so the signal transmission quality is optimized The effect is not satisfactory. It is recommended to reduce the interference in the network communication process based on the anti-window. First, the input communication signal is Pulley converted, the frequency domain interference detection is performed on the communication signal, a low-pass prototype filter is formed, the average value of the signal in each place is calculated, the interference reduction threshold is set, and the network communication process is completed. The weakening of interference realizes the optimization of transmission quality. This article conducts research and discussion on the optimization of the long-distance transmission quality of the communication signal of the 5G network. Experimental research results show that by demonstrating the optimization of the quality of the 5G communication signal transmission process, these demonstrations can ensure the quality of the communication signal, and people’s demand for the network is already indispensable.