scholarly journals Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2753 ◽  
Author(s):  
Robin Chataut ◽  
Robert Akl
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Visible Light Communication ◽  
Future Research ◽  
User Scheduling ◽  
Comprehensive Overview ◽  
Research Issues ◽  
Recent Trends

The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G—and beyond—networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.

scholarly journals Massive MIMO Systems for 5G

2020 ◽  
Author(s):  
Robin Chataut ◽  
Robert Akl
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Visible Light Communication ◽  
User Scheduling ◽  
Access Technology ◽  
Input Multiple Output ◽  
Simple Processing

The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues such as pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection is vital for the successful deployment of 5G and beyond networks. Some of the recent trends in massive MIMO are terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning. 

scholarly journals Maximizing Energy Efficiency for Consumption Circuit Power in Downlink Massive MIMO Wireless Networks

2017 ◽  
Vol 7 (6) ◽  
pp. 2977 ◽  
Author(s):  
Adeeb Salh ◽  
Lukman Audah ◽  
Nor Shahida M. Shah ◽  
Shipun A. Hamzah
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Data Rate ◽  
Optimal Number ◽  
Zero Forcing ◽  
High Data ◽  
Circuit Power

<span>Massive multi-input–multi-output (MIMO) systems are crucial to maximizing energy efficiency (EE) and battery-saving technology. Achieving EE without sacrificing the quality of service (QoS) is increasingly important for mobile devices. We first derive the data rate through zero forcing (ZF) and three linear precodings: maximum ratio transmission (MRT), zero forcing (ZF), and minimum mean square error (MMSE). Performance EE can be achieved when all available antennas are used and when taking account of the consumption circuit power ignored because of high transmit power. The aim of this work is to demonstrate how to obtain maximum EE while minimizing power consumed, which achieves a high data rate by deriving the optimal number of antennas in the downlink massive MIMO system. This system includes not only the transmitted power but also the fundamental operation circuit power at the transmitter signal. Maximized EE depends on the optimal number of antennas and determines the number of active users that should be scheduled in each cell. We conclude that the linear precoding technique MMSE achieves the maximum EE more than ZF and MRT</span><em></em><span>because the MMSE is able to make the massive MIMO system less sensitive to SNR at an increased number of antennas</span><span>.</span>

scholarly journals Energy Efficiency Augmentation in Massive MIMO Systems through Linear Precoding Schemes and Power Consumption Modeling

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rao Muhammad Asif ◽  
Jehangir Arshad ◽  
Mustafa Shakir ◽  
Sohail M. Noman ◽  
Ateeq Ur Rehman
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Antenna Arrays ◽  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Mimo System ◽  
Base Station ◽  
Total Power ◽  
Minimum Mean Squared Error

Massive multiple-input multiple-output or massive MIMO system has great potential for 5th generation (5G) wireless communication systems as it is capable of providing game-changing enhancements in area throughput and energy efficiency (EE). This work proposes a realistic and practically implementable EE model for massive MIMO systems while a general and canonical system model is used for single-cell scenario. Linear processing schemes are used for detection and precoding, i.e., minimum mean squared error (MMSE), zero-forcing (ZF), and maximum ratio transmission (MRT/MRC). Moreover, a power dissipation model is proposed that considers overall power consumption in uplink and downlink communications. The proposed model includes the total power consumed by power amplifier and circuit components at the base station (BS) and single antenna user equipment (UE). An optimal number of BS antennas to serve total UEs and the overall transmitted power are also computed. The simulation results confirm considerable improvements in the gain of area throughput and EE, and it also shows that the optimum area throughput and EE can be realized wherein a larger number of antenna arrays at BS are installed for serving a greater number of UEs.

scholarly journals Towards Optimal Energy Efficiency in Cell-Free Massive MIMO Systems

2021 ◽  
pp. 1-1
Author(s):  
A. Papazafeiropoulos ◽  
H. Q. Ngo ◽  
P. Kourtessis ◽  
S. Chatzinotas ◽  
J. M. Senior
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Optimal Energy

scholarly journals The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1552
Author(s):  
Tongzhou Han ◽  
Danfeng Zhao
Keyword(s):  
Power Control ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Time Varying ◽  
Input Multiple Output ◽  
Efficiency Performance ◽  
A Cell ◽  
Time Varying Channel

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

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