Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

Abstract

Future wireless communications are becoming increasingly complex with different radio access technologies, transmission backhauls, and network slices, and they play an important role in the emerging edge computing paradigm, which aims to reduce the wireless transmission latency between end-users and edge clouds. Deep learning techniques, which have already demonstrated overwhelming advantages in a wide range of internet of things (IoT) applications, show significant promise for solving such complicated real-world scenarios. Although the convergence of radio access networks and deep learning is still in the preliminary exploration stage, it has already attracted tremendous concern from both academia and industry. To address emerging theoretical and practical issues, ranging from basic concepts to research directions in future wireless networking applications and architectures, this paper mainly reviews the latest research progress and major technological deployment of deep learning in the development of wireless communications. We highlight the intuitions and key technologies of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation and compression sensing, encoding and decoding, and security and privacy. Main challenges, potential opportunities and future trends in incorporating deep learning schemes in wireless communications environments are further illustrated.further fulfill the requirements of future wireless communication systems, e.g., beyond the fifth-generation (B5G) networks.Along with the fast convergence of communication and computing in popular paradigms of edge computing and cloud computing [7,8], intelligent communication is considered to be one of the mainstream directions for the extensive development of future 5G and beyond wireless networks, since it can optimize wireless communication systems performance. In addition, with tremendous progress in artificial intelligence (AI) technology, it offers alternative options for addressing these challenges and replacing the design concepts of conventional wireless communications. Deep learning (DL) is playing an increasingly crucial role in the field of wireless communications due to its high efficiency in dealing with tremendous complex calculations, and is regarded as one of the effective tools for dealing with communication issues. Although deep learning has performed well in some IoT applications, "no free lunch" theorem [9] shows that a model cannot solve all problems once and for all, and we cannot learn a general model for a wide range of communication scenarios. This means that for any particular mobile and wireless network issue, we still need to adopt different deep learning architectures such as convolutional neural networks (CNN), deep neural networks (DNN) and recurrent neural networks (RNN), in order to achieve better performance of the communication systems.As a classic model of deep learning, autoencoder is widely used in the design paradigms of communication system ...

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