面向未来无线网络的迁移学习研究综述
Transfer Learning for Future Wireless Networks: A Comprehensive Survey
摘要：
凭借出色的功能，机器学习（ML）已成为无线网络中众多应用的支柱。然而，传统的ML方法在实际实现中面临着许多挑战，例如缺乏标记数据，不断变化的无线环境，训练过程长，以及无线设备容量有限。如果这些挑战得不到解决，将阻碍ML在未来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）最近出现了一个非常有前途的解决方案。TL的核心思想是利用和综合从类似任务中提炼出来的知识，以及从过去积累的宝贵经验，以促进新问题的学习。这样，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调查。特别是，我们首先提供了一个概述，包括正式的定义，分类，和各种类型的TL技术。然后，我们讨论了不同的TL方法提出解决无线网络中出现的问题。这些问题包括频谱管理、定位、信号识别、安全性、人类活动识别和缓存，这些都对5G等下一代网络非常重要。最后，我们强调了重要的挑战，开放的问题，和未来的研究方向TL在未来的无线网络。
英文摘要：
With outstanding features, Machine Learning (ML) has been the backbone of numerous applications in wireless networks. However, the conventional ML approaches have been facing 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, will impede the effectiveness and applicability of ML in future wireless networks. To address these problems, Transfer Learning (TL) has recently emerged to be a very promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks as well as from valuable experiences accumulated from the past to facilitate the learning of new problems. 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 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, localization, signal recognition, security, human activity recognition and caching, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
