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
收藏
页码:180928 / 180954
页数:27
相关论文
共 50 条
  • [1] Massive Access in Media Modulation Based Massive Machine-Type Communications
    Qiao, Li
    Zhang, Jun
    Gao, Zhen
    Ng, Derrick Wing Kwan
    Di Renzo, Marco
    Alouini, Mohamed-Slim
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2022, 21 (01) : 339 - 356
  • [2] Iterative List Detection and Decoding for Massive Machine-Type Communications
    Di Renna, Roberto B.
    de Lamare, Rodrigo C.
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2020, 68 (10) : 6276 - 6288
  • [3] Device Activity Detection and Non-Coherent Information Transmission for Massive Machine-Type Communications
    Tang, Zihan
    Wang, Jun
    Wang, Jintao
    Song, Jian
    IEEE ACCESS, 2020, 8 : 41452 - 41465
  • [4] Coexistence of Human-Type and Machine-Type Communications in Uplink Massive MIMO
    Kuai, Xiaoyan
    Yuan, Xiaojun
    Yan, Wenjing
    Liang, Ying-Chang
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2021, 39 (03) : 804 - 819
  • [5] Exploiting prior information for greedy compressed sensing based detection in machine-type communications
    Lee, Kyubihn
    Yu, Nam Yul
    DIGITAL SIGNAL PROCESSING, 2020, 107
  • [6] A Grant-Free Method for Massive Machine-Type Communication With Backward Activity Level Estimation
    Xiao, Han
    Chen, Wei
    Fang, Jun
    Ai, Bo
    Wassell, Ian J.
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2020, 68 : 6665 - 6680
  • [7] Decentralized Channel Estimation for the Uplink of Grant-Free Massive Machine-Type Communications
    Liu, Songbin
    Zhang, Haochuan
    Zou, Qiuyun
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (02) : 967 - 979
  • [8] A Study of Random Access for Massive Machine-type Communications: Limitations and Solutions
    Youn, Jiseung
    Park, Joohan
    Kim, Soohyeong
    You, Cheolwoo
    Cho, Sunghyun
    2021 IEEE REGION 10 SYMPOSIUM (TENSYMP), 2021,
  • [9] Estimation of user activity prior for active user detection in massive machine type communications
    Irtaza, Syed Ali
    Riaz, Salma
    Nauman, Ali
    Jamshed, Muhammad Ali
    Kim, Sung Won
    SIGNAL PROCESSING, 2023, 205
  • [10] EP-Based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications
    Ahn, Jinyoup
    Shim, Byonghyo
    Lee, Kwang Bok
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2019, 67 (07) : 5178 - 5189