Recent advances in data-driven wireless communication using Gaussian processes: A comprehensive survey
Recent advances in data-driven wireless communication using Gaussian processes: A comprehensive survey
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DOI：
10.23919/jcc.2022.01.016
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发表时间：
2021-03
影响因子：
4.1
通讯作者：
Kai Chen;Qinglei Kong;Yijue Dai;Yue Xu;Feng Yin;Lexi Xu;Shuguang Cui
中科院分区：
文献类型：
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作者：
Kai Chen;Qinglei Kong;Yijue Dai;Yue Xu;Feng Yin;Lexi Xu;Shuguang Cui
数据驱动的范式是众所周知的，也是未来无线通信的突出需求。在大数据和机器学习技术的支持下，下一代数据驱动的通信系统将是智能化的，具有表达性、可扩展性、可解释性和不确定性感知等独特特征，可以在可预见的未来自信地涉及多样化的潜在需求和个性化服务。在本文中，我们回顾了一系列有前途的非参数贝叶斯机器学习模型，即高斯过程（GP）及其在无线通信中的应用。由于GP模型表现出出色的表达性和可解释性的不确定性学习能力，因此特别适合无线通信。此外，它们还为协作数据和经验模型（DEM）提供了一个自然的框架。具体来说，我们首先设想使用 GP 模型的数据驱动无线通信的三层动机。然后，我们从协方差结构和模型推理方面介绍了 GP 的背景。展示了使用各种可解释内核（包括静态、非静态、深度和多任务内核）的 GP 模型的表达能力。此外，我们回顾了具有良好可扩展性的分布式GP模型，它适用于具有大量分布式边缘设备的无线网络中的应用。最后，我们列出了在各种无线通信应用中采用 GP 模型的代表性解决方案和有前景的技术。
Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques, next-generation data-driven communication systems will be intelligent with unique characteristics of expressiveness, scalability, interpretability, and uncertainty awareness, which can confidently involve diversified latent demands and personalized services in the foreseeable 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 demonstrate 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 empirical models (DEM). Specifically, we first envision three-level motivations of data-driven wireless communication 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 suitable 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.