期刊
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
卷 37, 期 10, 页码 2184-2199出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2019.2933969
关键词
Autoencoders; channel coding; channel estimation; data-driven methods; distributed learning; distributed resource allocation; deep learning; federated edge learning; joint source-channel coding; machine learning; stochastic approximation; wireless communications
资金
- European Research Council (ERC) under the European Union [677854]
- ERC under the European Union [670896]
- NSF [CIF-1525194, ECCS-1608961, ECCS-1807660]
- Ministry of Electronics and Information Technology, Government of India
Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据