HUMAN AND MACHINE TYPE COMMUNICATIONS CAN COEXIST IN UPLINK MASSIVE MIMO SYSTEMS

被引:0
作者
Senel, Kamil [1 ]
Bjornson, Emil [1 ]
Larsson, Erik G. [1 ]
机构
[1] Linkoping Univ, Dept Elect Engn, Linkoping, Sweden
来源
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2018年
基金
瑞典研究理事会;
关键词
MIMO; Machine Type Communication; ACCESS;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Future cellular networks are expected to support new communication paradigms such as machine-type communication (MTC) services along with human-type communication (HTC) services. This requires base stations to serve a large number of devices in relatively short channel coherence intervals which renders allocation of orthogonal pilot sequence per-device approaches impractical. Furthermore. the stringent power constraints, place-and-play type connectivity and various data rate requirements of MTC devices make it impossible for the traditional cellular architecture to accommodate MTC and HTC services together. Massive multiple-input-multiple-output (MaMIMO) technology has the potential to allow the coexistence of HTC and MTC services, thanks to its inherent spatial multiplexing properties and low transmission power requirements. In this work, we investigate the performance of a single cell under a shared physical channel assumption for MTC and HTC services and propose a novel scheme for sharing the time-frequency resources. The analysis reveals that MaMIMO can significantly enhance the performance of such a setup and allow the inclusion of MTC services into the cellular networks without requiring additional resources.
引用
收藏
页码:6613 / 6617
页数:5
相关论文
共 29 条
  • [21] 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
  • [22] Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications
    Ma, Zhe
    Wu, Wen
    Gao, Feifei
    Shen, Xuemin
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2024, 23 (03) : 2197 - 2211
  • [23] Cloud-Based Cell-Free Massive MIMO Systems: Uplink Error Probability Analysis and Near-Optimal Detector Design
    Zhang, Yu
    Xiao, Lixia
    Jiang, Tao
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (02) : 797 - 809
  • [24] A Dynamic Backoff Window Scheme for Machine-Type Communications in Cyber-Physical Systems
    Chen, Jenhui
    Cheng, Ray-Guang
    Agbodike, Obinna
    Lyu, Yu-Syuan
    IEEE ACCESS, 2020, 8 : 31045 - 31056
  • [25] A Study on the Application of Rateless Coding in Non-Cellular MIMO Systems for Machine-Type Communication
    Karrenbauer, Michael
    Melnyk, Sergiy
    Krummacker, Dennis
    Weinand, Andreas
    Schotten, Hans D.
    IFAC PAPERSONLINE, 2020, 53 (02): : 8243 - 8248
  • [26] MACHINE-TYPE COMMUNICATIONS: CURRENT STATUS AND FUTURE PERSPECTIVES TOWARD 5G SYSTEMS
    Shariatmadari, Hamidreza
    Ratasuk, Rapeepat
    Iraji, Sassan
    Laya, Andres
    Taleb, Tarik
    Jaentti, Riku
    Ghosh, Amitava
    IEEE COMMUNICATIONS MAGAZINE, 2015, 53 : 10 - 17
  • [27] Deep Adaptive Learning-Based Beam Combining Framework for 5G Millimeter-Wave Massive 3D-MIMO Uplink Systems
    Mahendran, K.
    Sudarsan, H.
    Rathika, S.
    Shankarlal, B.
    TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2024, 35 (12):
  • [28] Pilot-Efficient Scheduling for Large-Scale Antenna Aided Massive Machine-Type Communications: A Cross-Layer Approach
    Xie, Zhanyuan
    Chen, Wei
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2020, 68 (07) : 4262 - 4276
  • [29] Improved Compressed Sensing-Based Joint User and Symbol Detection for Media-Based Modulation-Enabled Massive Machine-Type Communications
    Ma, Xiangxue
    Guo, Shuaishuai
    Yuan, Dongfeng
    IEEE ACCESS, 2020, 8 : 70058 - 70070