With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods’ robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

中文翻译：

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无线网络的迁移学习：综合调查


机器学习（ML）凭借出色的特性，已成为无线网络众多应用的支柱。然而，传统的机器学习方法在实际实施中面临着许多挑战，例如缺乏标记数据、不断变化的无线环境、漫长的训练过程以及无线设备的容量有限等。如果不解决这些挑战，可能会阻碍机器学习在无线网络中的有效性和适用性。为了解决这些问题，迁移学习（TL）最近成为一种有前途的解决方案。 TL的核心思想是利用和综合从类似任务中提取的知识和过去积累的宝贵经验，以促进新问题的学习。通过这样做，TL技术可以减少对标记数据的依赖，提高学习速度，并增强ML方法对不同无线环境的鲁棒性。本文旨在对 TL 在无线网络中的应用进行全面的综述。特别是，我们首先提供了 TL 的概述，包括形式定义、分类和各种类型的 TL 技术。然后，我们讨论为解决无线网络中新出现的问题而提出的各种 TL 方法。这些问题包括频谱管理、信号识别、安全、缓存、本地化和人类活动识别，这些对于下一代网络（例如 5G 及更高版本）都很重要。最后，我们强调了 TL 在未来无线网络中的重要挑战、未解决的问题和未来的研究方向。