Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions

被引：13

作者：

Di Renna, Roberto B. ^{[1
]}

Bockelmann, Carsten ^{[2
]}

de Lamare, Rodrigo C. ^{[1
]}

Dekorsy, Armin ^{[1
]}

机构：

[1] Pontifical Catholic Univ Rio de Janeiro PUC Rio, Ctr Telecommun Studies CETUC, BR-22453900 Rio De Janeiro, Brazil

[2] Univ Bremen, Dept Commun Engn, D-28359 Bremen, Germany

来源：

IEEE ACCESS | 2020年 / 8卷 / 08期

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; SUCCESSIVE INTERFERENCE CANCELLATION; SPARSE ACTIVITY DETECTION; USER ACTIVITY DETECTION; CHANNEL ESTIMATION; MULTIUSER DETECTION; MULTIPLE-ACCESS; PART I; RECEIVER DESIGN; SIGNAL RECOVERY; LEAST-SQUARES;

D O I：

10.1109/ACCESS.2020.3027523

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：180928 / 180954

页数：27

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