With the rapid growth of wireless data traffic and antennas configuration, higher spectrum efficiency and lower power consumption processing have evoked remarkable attention from the research and industry community for the deployment of future wireless communication. It has become a heated topic quickly in recent years and gives rise to the widespread interest around the world. As a core technology of the fifth-generation (5G) mobile communication, massive multi-input multi-output (MIMO) technology can fully exploit the space resources and greatly improve the spectral and energy efficiency. However, massive MIMO systems are faced with the problems of mass data processing, high hardware cost, and huge total power consumption. To cope with these problems, a useful solution is that the receiver equips with finite resolution analog-to-digital (ADC) converters. A large number of research results show that the low-resolution quantization technology brings significant performance within the allowable loss of capacity. This promising technique has attracted many scholars to do tremendous endeavor on it. As a motivation, we make a comprehensive survey about low-resolution ADCs for wireless communication. This paper summarizes the latest developments in the design of low-resolution communication systems, focusing on system performance analysis, some key technologies of the receiver, and typical application scenarios for the low-resolution ADCs. In view of the adverse effects caused by coarse quantization, some potential implementations are presented to alleviate this dilemma. Future research directions are also given and suggested in this paper. This overview contributes significantly to providing an informative and tutorial reference for the key technologies of low-resolution ADCs as well as its applications in practical systems.

With the rapid proliferation of wireless services, the frequency spectrum has become increasingly crowded, and the interferences and composite signals will be ubiquitous in the wireless receiver. For deeply dissecting and detecting the expected signals, the research community has to investigate the smart signal processing technology to resisting the influence of detrimental signals. For this purpose, blind source separation has been shown to be a promising method for achieving simultaneous spectrum utilization and wireless adaptive interference cancellation. The attractive features and appealing advantages of blind source separation make it an attractive theory for source extraction or recovery, which plays a crucial role in helping realize intelligent signal processing for wireless communication. It can recover the unobserved sources only from the wireless received mixed signals based on the features of the source signal exempted from channel estimation and synchronization manipulation. Wireless communication systems can benefit the high spectrum efficiency, strong anti-interference, and adaptive signal processing through the blind separation mechanism. So far, numerous researchers have made tremendous efforts to investigate this field for enhancing spectrum efficiency, anti-interference ability, and signal detection performance through employing the philosophy of blind separation. These meaningful and appealing research works motivate us to make a comprehensive survey with regard to this area. In this paper, the fundamental principle of the blind separation mechanism, involving independent component analysis, sparse component analysis, non-negative matrix factorization, and bounded component analysis will be reviewed briefly, and then, the critical technologies applied in various wireless communication systems will be overviewed, such as in direct-sequence code division multiplexing access, frequency hopping, orthogonal frequency-division multiplexing, multiple input multiple output, wireless sensor networks, cognitive radio networks, radio frequency identification devices, and communication security. In addition, the important research challenges and meaningful research directions pertaining to the area of blind separation applied in wireless communications systems are also discussed.

Since the emergence of 5G technology, the wireless communication system has had a huge data throughput, so the joint development of artificial intelligence technology and wireless communication technology is one of the current mainstream development directions. In particular the combination of deep learning technology and communication physical layer technology is the future research hotspot. The purpose of this research paper is to summarize the related algorithms of the combination of Automatic Modulation Recognition (AMR) technology and deep learning technology in the communication physical layer. In order to elicit the advantages of the modulation recognition algorithm based on deep learning, this paper firstly introduces the traditional AMR method, and then summarizes the advantages and disadvantages of the traditional algorithm. Then, the application of the deep learning algorithm in AMR is described, and the identification method based on a typical deep learning network is emphatically described. Afterwards, the existing Deep Learning (DL) modulation identification algorithm in a small sample environment is summarized. Finally, DL modulation is discussed, identifying field challenges, and future research directions.

In recent years, with the rapid development in wireless communication and 5G networks, the rapid growth in mobile users has been accompanied by an increasing demand for the electromagnetic spectrum. The birth of cognitive radio and its spectrum-sensing technology provides hope for solving the problem of low utilization of the wireless spectrum. Artificial intelligence (AI) has been widely discussed globally. Deep learning technology, known for its strong learning ability and adaptability, plays a significant role in this field. Moreover, integrating deep learning with wireless communication technology has become a prominent research direction in recent years. The research objective of this paper is to summarize the algorithm of cognitive radio spectrum-sensing technology combined with deep learning technology. To review the advantages of deep-learning-based spectrum-sensing algorithms, this paper first introduces the traditional spectrum-sensing methods. It summarizes and compares the advantages and disadvantages of each method. It then describes the application of deep learning algorithms in spectrum sensing and focuses on the typical deep-neural-network-based sensing methods. Then, the existing deep-learning-based cooperative spectrum-sensing methods are summarized. Finally, the deep learning spectrum-sensing methods are discussed, along with challenges in the field and future research directions.

Infrared and visible light image fusion combines infrared and visible light images by extracting the main information from each image and fusing it together to provide a more comprehensive image with more features from the two photos. Infrared and visible image fusion has gained popularity in recent years and is increasingly being employed in sectors such as target recognition and tracking, night vision, scene segmentation, and others. In order to provide a concise overview of infrared and visible picture fusion, this paper first explores its historical context before outlining current domestic and international research efforts. Then, conventional approaches for infrared and visible picture fusion, such as the multi-scale decomposition method and the sparse representation method, are thoroughly introduced. The advancement of deep learning in recent years has greatly aided the field of picture fusion. The outcomes of the fusion have a wide range of potential applications due to the neural networks’ strong feature extraction and reconstruction skills. As a result, this research also evaluates deep learning techniques. After that, some common objective evaluation indexes are provided, and the performance evaluation of infrared and visible image fusion is introduced. The common datasets in the areas of infrared and visible image fusion are also sorted out at the same time. Datasets play a significant role in the advancement of infrared and visible image fusion and are an essential component of infrared and visible image fusion testing. The application of infrared and visible image fusion in many domains is then simply studied with practical examples, particularly in developing fields, used to show its application. Finally, the prospect of the current infrared and visible image fusion field is presented, and the full text is summarized.

With the development of science and technology and the progress of the times, automation and intelligence have been popularized in manufacturing in all walks of life. With the progress of productivity, product defect detection has become an indispensable part. However, in practical scenarios, the application of supervised deep learning algorithms in the field of defect detection is limited due to the difficulty and unpredictability of obtaining defect samples. In recent years, semi-supervised and unsupervised deep learning algorithms have attracted more and more attention in various defect detection tasks. Generative adversarial networks (GAN), as an unsupervised learning algorithm, has been widely used in defect detection tasks in various fields due to its powerful generation ability. In order to provide some inspiration for the researchers who intend to use GAN for defect detection research. In this paper, the theoretical basis, technical development and practical application of GAN based defect detection are reviewed. This paper also discusses the current outstanding problems of GAN and GAN-based defect detection, and makes a detailed prediction and analysis of the possible future research directions. This paper summarizes the relevant literature on the research progress and application status of GAN based defect detection, which provides certain technical information for researchers who are interested in researching GAN and hope to apply it to defect detection tasks.