mlsanityData to 7 Oct 2026

Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey

arXiv 2103.10134 ↗First posted 18 Mar 2021cs.LG cs.SY eess.SYcited by 5

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Abstract (arXiv)

Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially uncertainty modeling, 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 methods, i.e., Gaussian processes (GPs), and their applications in wireless communication. Since GPs achieve the expressive and interpretable learning ability with uncertainty, it is particularly suitable for wireless communication. Moreover, it provides a natural framework for collaborating data and empirical models (DEM). Specifically, we first envision three-level motivations of data-driven wireless communication using GPs. 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 kernel designs is surveyed, namely, stationary, non-stationary, deep, and multi-task kernels. Furthermore, we review the distributed GPs 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 GPs in wireless communication systems.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 41 of its 124 references that are in the map. First posted on arXiv 18 Mar 2021.

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41 of 124 references.

References · 41

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29 more
  1. 2018Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regressioncited by 33
  2. 2017Spectral Mixture Kernels for Multi-Output Gaussian Processescited by 29
  3. 2016Learning Scalable Deep Kernels with Recurrent Structurecited by 22
  4. 2014Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Modelscited by 19
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  8. 2014Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regressioncited by 15
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  13. 2019Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Seriescited by 9
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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 1.

5 papers.

  1. 2022Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modelingcited by 12
  2. 2021Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modelingcited by 8
  3. 2024Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Predictioncited by 3
  4. 2023Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernelcited by 2
  5. 2025Enhancing Zero-Shot Time Series Forecasting in Off-the-Shelf LLMs via Noise Injectioncited by 0