Quantized maximum ratio transmission for multiple-input multiple-output wireless systems

2003 ◽  
Author(s):  
D.J. Love ◽  
R.W. Heath ◽  
T. Strohmer
Keyword(s):  
Multiple Input Multiple Output ◽  
Wireless Systems ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ratio Transmission ◽  
Maximum Ratio Transmission

scholarly journals Performance enhancement of maximum ratio transmission in 5G system with multi-user multiple-input multiple-output

2022 ◽  
Vol 12 (2) ◽  
pp. 1650
Author(s):  
Sarmad K. Ibrahim ◽  
Saif A. Abdulhussien
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Performance Enhancement ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Standard System ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ratio Transmission ◽  
Maximum Ratio Transmission

<span>The downlink multi-user precoding of the multiple-input multiple-output (MIMO) method includes optimal channel state information at the base station and a variety of linear precoding (LP) schemes. Maximum ratio transmission (MRT) is among the common precoding schemes but does not provide good performance with massive MIMO, such as high bit error rate (BER) and low throughput. The orthogonal frequency division multiplexing (OFDM) and precoding schemes used in 5G have a flaw in high-speed environments. Given that the Doppler effect induces frequency changes, orthogonality between OFDM subcarriers is disrupted and their throughput output is decreased and BER is decreased. This study focuses on solving this problem by improving the performance of a 5G system with MRT, specifically by using a new design that includes weighted overlap and add (WOLA) with MRT. The current research also compares the standard system MRT with OFDM with the proposed design (WOLA-MRT) to find the best performance on throughput and BER. Improved system results show outstanding performance enhancement over a standard system, and numerous improvements with massive MIMO, such as best BER and throughput. Its approximately 60% more throughput than the traditional systems. Lastly, the proposed system improves BER by approximately 2% compared with the traditional system.</span>

The Security Risks and Challenges of 5G Communications

2021 ◽  
Vol 3 (2) ◽  
pp. 46-53
Author(s):  
Young B. Choi ◽  
Matthew E. Bunn
Keyword(s):  
Multiple Input Multiple Output ◽  
Wireless Systems ◽  
Daily Basis ◽  
Automated Manufacturing ◽  
Security Risks ◽  
Security Challenges ◽  
Autonomous Cars ◽  
Multiple Input ◽  
Input Multiple Output ◽  
The Internet Of Things

With the introduction of the 5th generation of wireless systems and communications (5G) comes new risks and challenges. This paper explores the potential security challenges of 5G communication compared with legacy cellular networks and prior generations of communication standards. This paper defines what 5G is and how it affects our lives on a daily basis. It further discusses the new security features involving different technologies applied to 5G, such as heterogeneous networks, device-to-device communications, massive multiple-input multiple-output, software-defined networks, and the internet of things, including autonomous cars, healthcare, automated manufacturing, and more.

scholarly journals An improvement to hybrid beamforming precoding scheme for mmWave massive MIMO systems based on channel matrix

2021 ◽  
Vol 23 (1) ◽  
pp. 285
Author(s):  
Zahra Amirifar ◽  
Jamshid Abouei
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Wireless Systems ◽  
Achievable Rate ◽  
Percentage Difference ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Hybrid Beamforming ◽  
Mimo Technology

<p>The massive multiple-input multiple-output (MIMO) technology has been applied innew generation wireless systems due to growing demand for reliability and high datarate. Hybrid beamforming architectures in both receiver and transmitter, includinganalog and digital precoders, play a significant role in 5G communication networksand have recently attracted a lot of attention. In this paper, we propose a simple andeffective beamforming precoder approach for mmWave massive MIMO systems. Wefirst solve an optimization problem by a simplification subject, and in the second step,we use the covariance channel matrixfCk=Cov(Hk)andBk=HkHHkinstead of chan-nel matrixHk. Simulation results verify that the proposed scheme can enjoy a highersum rate and energy efficiency than previous methods such as spatially sparse method,analog method, and conventional hybrid method even with inaccurate Channel StateInformation (CSI). Percentage difference of the achievable rate ofCk=Cov(Hk)andBk=HkHHkschemes compared to conventional methods are 2.51% and 48.94%, re-spectively.</p>

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