期刊
IEEE ACCESS
卷 8, 期 -, 页码 180928-180954出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3027523
关键词
Channel estimation; Matching pursuit algorithms; Detectors; Machine learning; Machine learning algorithms; Inference algorithms; Security; 5G; channel estimation; detection; massive access; mMTC; random access
资金
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES) [001]
Massive machine-type communications (mMTC) is one of the key application scenarios of fifth generation (5G) and beyond cellular networks. Bringing the unique technical challenge of supporting a huge number of MTC devices (MTCD) in cellular networks, how to efficiently estimate the channel, detect the active users and data in this scenario is an open research topic. In this regard, this paper aims to present an overview of different techniques to address the problem of channel estimation, activity and data detection specifically for the mMTC scenario. In order to highlight potential solutions and to propose new research directions, we discuss the performance of the state-of-the-art techniques in the literature using a unified evaluation framework.
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