Data-driven paradigms are well-known and salient demands of future wireless communication.Em-powered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expres-siveness,scalability,interpretability,and uncertainty awareness,which can confidently involve diversified latent demands and personalized services in the foresee-able future.In this paper,we review a promising family of nonparametric Bayesian machine learning models,i.e.,Gaussian processes (GPs),and their applications in wireless communication.Since GP models demon-strate outstanding expressive and interpretable learning ability with uncertainty,they are particularly suitable for wireless communication.Moreover,they provide a natural framework for collaborating data and em-pirical models (DEM).Specifically,we first envision three-level motivations of data-driven wireless com-munication using GP models.Then,we present the background of the GPs in terms of covariance structure and model inference.The expressiveness of the GP model using various interpretable kernels,including stationary,non-stationary,deep and multi-task kernels,is showcased.Furthermore,we review the distributed GP models with promising scalability,which is suit-able for applications in wireless networks with a large number of distributed edge devices.Finally,we list representative solutions and promising techniques that adopt GP models in various wireless communication applications.

Kai Chen;Qinglei Kong;Yijue Dai;Yue Xu;Feng Yin;Lexi Xu;Shuguang Cui

Future Network of Intelligence Institute (FNii),The Chinese University of Hong Kong,Shenzhen 518172,China;School of Information Science and Technology,University of Science and Technology of China,Hefei 230026,China;School of Science and Engineering,The Chinese University of Hong Kong,Shenzhen 518172,China;Alibaba Group,Hangzhou 310052,China;Research Institute,China United Network Communications Corporation,Beijing 100048,China

中国通信（英文版）

[1]Kai Chen;Qinglei Kong;Yijue Dai;Yue Xu;Feng Yin;Lexi Xu;Shuguang Cui-.Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes:A Comprehensive Survey)[J].中国通信（英文版）,2022(01):218-237

foresee,expressiveness

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