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Fast Adaptation for Deep Learning-based Wireless Communications

Ouya Wang, Hengtao He, Shenglong Zhou, Zhi Ding, Shi Jin, Khaled B. Letaief, Geoffrey Ye Li

TL;DR

This article presents a comprehensive review of the existing FSL techniques in wireless communications that satisfy two distinct FSL design requirements for wireless communications and emphasizes the importance of applying domain knowledge in achieving fast adaptation.

Abstract

The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in wireless communications. In particular, existing DL-based wireless communications struggle to adapt to the rapidly changing wireless environments. In this paper, we discuss fast adaptation for DL-based wireless communications by using few-shot learning (FSL) techniques. We first identify the differences between fast adaptation in wireless communications and traditional AI tasks by highlighting two distinct FSL design requirements for wireless communications. To establish a wide perspective, we present a comprehensive review of the existing FSL techniques in wireless communications that satisfy these two design requirements. In particular, we emphasize the importance of applying domain knowledge in achieving fast adaptation. We specifically focus on multiuser multiple-input multiple-output (MU-MIMO) precoding as an examples to demonstrate the advantages of the FSL to achieve fast adaptation in wireless communications. Finally, we highlight several open research issues for achieving broadscope future deployment of fast adaptive DL in wireless communication applications.

Fast Adaptation for Deep Learning-based Wireless Communications

TL;DR

This article presents a comprehensive review of the existing FSL techniques in wireless communications that satisfy two distinct FSL design requirements for wireless communications and emphasizes the importance of applying domain knowledge in achieving fast adaptation.

Abstract

The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in wireless communications. In particular, existing DL-based wireless communications struggle to adapt to the rapidly changing wireless environments. In this paper, we discuss fast adaptation for DL-based wireless communications by using few-shot learning (FSL) techniques. We first identify the differences between fast adaptation in wireless communications and traditional AI tasks by highlighting two distinct FSL design requirements for wireless communications. To establish a wide perspective, we present a comprehensive review of the existing FSL techniques in wireless communications that satisfy these two design requirements. In particular, we emphasize the importance of applying domain knowledge in achieving fast adaptation. We specifically focus on multiuser multiple-input multiple-output (MU-MIMO) precoding as an examples to demonstrate the advantages of the FSL to achieve fast adaptation in wireless communications. Finally, we highlight several open research issues for achieving broadscope future deployment of fast adaptive DL in wireless communication applications.
Paper Structure (18 sections, 1 equation, 5 figures, 1 table)

This paper contains 18 sections, 1 equation, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. FSL in AI and Wireless Communications
  3. FSL Techniques for Traditional AI Tasks
  4. Challenges of FSL in Wireless Communications
  5. Characteristics and Requirements of FSL in Wireless Communications
  6. Data-Driven FSL for Wireless Communications
  7. Meta Learning
  8. Multi-Task Learning
  9. Domain Knowledge-based FSL Techniques
  10. Feature-Driven DL
  11. Model-Driven DL
  12. Deep Unfolding
  13. DNN-aided model-driven DL
  14. Numerial Results
  15. Conclusion and Future Research Directions
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Categories of FSL techniques in wireless communications.
  • Figure 2: Structure of hypernetwork to generate parameters for channel estimation network.
  • Figure 3: Structure of multi-task meta learning. $\phi$ and $\varphi$ refer to the DL-based system and the hypernetwork.
  • Figure 4: The structures of feature-driven DL, deep unfolding, and DNN-aided model-driven DL designed for both iteration or non-iteration algorithms. The FPN is developed for iterative algorithms, and the NC framwork is designed for non-iterative algorithms.
  • Figure 5: The fast adaptation performance of new tasks, using learning algorithms and domain knowledge. The experiment "Upperbound" refers to the scenario where sufficient data is available in the new environment to train the deep unfolding network.