A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> From ML to DL. <s> Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> From ML to DL. <s> Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> From ML to DL. <s> In the last few years, we have witnessed an exponential growth in research activity into the advanced training of convolutional neural networks (CNNs), a field that has become known as deep learning. This has been triggered by a combination of the availability of significantly larger data sets, thanks in part to a corresponding growth in big data, and the arrival of new graphics-processing-unit (GPU)-based hardware that enables these large data sets to be processed in reasonable timescales. Suddenly, a wide variety of long-standing problems in machine learning, artificial intelligence, and computer vision have seen significant improvements, often sufficient to break through long-standing performance barriers. Across multiple fields, these achievements have inspired the development of improved tools and methodologies leading to even broader applicability of deep learning. The new generation of smart assistants, such as Alexa, Hello Google, and others, have their roots and learning algorithms tied to deep learning. In this article, we review the current state of deep learning, explain what it is, why it has managed to improve on the long-standing techniques of conventional neural networks, and, most importantly, how you can get started with adopting deep learning into your own research activities to solve both new and old problems and build better, smarter consumer devices and services. <s> BIB003
Although ML is not a new field, with the explosive growth of the amount of data and the development of hardware technology ML has been one of research hotspots in both academe and industry BIB001 BIB002 . Recently, ML has also made great strides in signal recognition. ML works well on automatic signal classification for small signal dataset under relatively ideal conditions  . Most algorithms are easy to interpret and have the advantage of low implementation cost. While for a high-dimensional dataset, machine learning is prone to curse of dimensionality and the complex feature engineering is time demanding. In terms of algorithm theory, ML has a risk of falling into local optimum, which may lead to great performance degradation. Furthermore, ML models are trained for specific solutions and lack of generality in different real field environments. To overcome these issues, DL is developed and achieves high accuracy and efficiency BIB003 . Owing to multilayered artificial neural networks, DL needs few data preprocessing and shows adaptive to different application scenarios. A comparison between ML and DL is summarized in Table 1 .