scholarly journals A Survey on Multi-Layer Mimo for Lte/Lte-Advanced

2018 ◽  
Vol 7 (3.34) ◽  
pp. 234
Author(s):  
Anjana Devi.J ◽  
Dr R. Dhaya ◽  
Dr R. Kanthavel
Keyword(s):  
System Performance ◽  
Massive Mimo ◽  
Mimo System ◽  
Spatial Diversity ◽  
Multiple Streams ◽  
Key Technology ◽  
Main Design ◽  
Lte Advanced ◽  
Lower Complexity ◽  
Mimo Technology

The primary intention of the paper is to explore on various Multi-Layer MIMO approaches for the higher system performance. MIMO technique is said to be the key technology in LTE-A to achieve the spatial diversity in the system. Multi-Layer MIMO is the enhancement to the MIMO technology where multiple streams of data could be transferred to different layers for optimization with increased capacity in the network in high SNR condition. In a massive MIMO system, the cell interference hinders the system performance due to high channel dimensionality. The main design objective of the Multi-Layer MIMO techniques studied is to reduce the Cell Interference with lower complexity.  

MIMO Systems in a Composite Fading and Generalized Noise Scenario: A Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Keerti Tiwari
Keyword(s):  
System Performance ◽  
Performance Enhancement ◽  
Channel Model ◽  
Mimo Systems ◽  
Mimo System ◽  
Spatial Diversity ◽  
Spatial Multiplexing ◽  
Channel Models ◽  
Efficient System ◽  
Composite Channel

: Multiple-input multiple-output (MIMO) systems have been endorsed to enable future wireless communication requirements. The efficient system designing appeals an appropriate channel model, that considers all the dominating effects of wireless environment. Therefore, some complex or less analytically acquiescent composite channel models have been proposed typically for single-input single-output (SISO) systems. These models are explicitly employed for mobile applications, though, we need a specific study of a model for MIMO system which can deal with radar clutters and different indoor/outdoor and mobile communication environments. Subsequently, the performance enhancement of MIMO system is also required in such scenario. The system performance enhancement can be examined by low error rate and high capacity using spatial diversity and spatial multiplexing respectively. Furthermore, for a more feasible and practical system modeling, we require a generalized noise model along with a composite channel model. Thus, all the patents related to MIMO channel models are revised to achieve the near optimal system performance in real world scenario. This review paper offers the methods to improve MIMO system performance in less and severe fading as well as shadowing environment and focused on a composite Weibull-gamma fading model. The development is the collective effects of selecting the appropriate channel models, spatial multiplexing/detection and spatial diversity techniques both at the transmitter and the receivers in the presence of arbitrary noise.

scholarly journals Analysis of the massive MIMO technology

2021 ◽  
Vol 2061 (1) ◽  
pp. 012094
Author(s):  
N S Druzhinina ◽  
I M Daudov
Keyword(s):  
Antenna Array ◽  
Massive Mimo ◽  
Significant Contribution ◽  
Mimo System ◽  
Mimo Technology

Abstract The article discusses the features of the Massive MIMO technology, the structure of the antenna array, as well as the advantages and example of using the massive MIMO system. The use of Massive MIMO opens up new opportunities and makes a significant contribution to achieving the stated requirements for the further evolution of LTE and 5G.

scholarly journals Combining Alamouti Scheme with Block Diagonalization Beamforming precoding for 5G Technology over Rician Channel

2019 ◽  
pp. 22-28
Author(s):  
Cebrail Ciflikli
Keyword(s):  
System Performance ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Block Code ◽  
Spatial Diversity ◽  
Block Diagonalization ◽  
Combined System ◽  
Alamouti Scheme ◽  
Input Multiple Output ◽  
Rician Channel

Wireless communication faces a number of adversities and obstacles as a result of fading and co-channel interference (CCI). Diversity with beamformer techniques may be used to mitigate degradation in the system performance. Alamouti space-time-block-code (STBC) is a strong scheme focused on accomplishing spatial diversity at the transmitter, which needs a straightforward linear processing in the receiver. Also, high bit-error-rate (BER) performance can be achieved by using the multiple-input multiple-output (MIMO) system with beamforming technology. This approach is particularly useful for CCI suppression. Exploiting the channel state information (CSI) at the transmitter can improve the STBC through the use of a beamforming precoding. In this paper, we propose the combination between Alamouti STBC and block diagonalization (BD) for downlink multi-user MIMO system. Also, this paper evaluates the system performance improvement of the extended Alamouti scheme, with the implementation of BD precoding over a Rayleigh and Rician channel. Simulation results show that the combined system has performance better than the performance of beamforming system. Also, it shows that the combined system performance of extended Alamouti outperforms the combined system performance without extended Alamouti. Furthermore, numerical results confirm that the Rician channel can significantly improve the combined system performance.

scholarly journals Guided MAC to Enhance Quality of Experience of Video Data Transmission over Massive MIMO

2021 ◽  
Author(s):  
Diwakar Bhardwaj ◽  
Keyword(s):  
Massive Mimo ◽  
High Speed ◽  
Quality Of Experience ◽  
Mimo System ◽  
Video Data ◽  
Mac Layer ◽  
Mimo Channels ◽  
Noisy Channels ◽  
Mimo Technology

Massive MIMO (M-MIMO) system comprises of multiple number of antennas to achieve energy- efficiency and large gains in spectral-efficiency in comparison to existing MIMO technology. High speed and Quality of Experience (QoE) of video data over wireless communication has always been a challenge for the researchers due to scarcity of the bandwidth, fading and interference. The channels with high noise corrupt the transmitted video and results in poor QoE of at the receiver. Therefore, to maintain the quality of transmitted video, it is highly desirable to identify noisy channels and avoid transmission over them. This paper deals with QoE of the transmitted video over Massive MIMO channels. The channels are categorized into two categories: good and bad depending upon the value of Signal to Interference and Noise Ratio (SINR). A channel above the minimum acceptable value (threshold) of SINR is categorized as good channel otherwise bad channel. A Guided MAC layer (GMAC) protocol is designed to transmit the video data over good channels only and to discard the transmission over bad channels.

scholarly journals Ergodic Capacity for Evaluation of Mobile System Performance

2020 ◽  
Vol 26 (10) ◽  
pp. 135-148 ◽  
Author(s):  
Daroon Shaho Omer ◽  
Mohammed Abdullah Hussein ◽  
Luqman Mahmood Mina
Keyword(s):  
System Performance ◽  
Massive Mimo ◽  
Rayleigh Fading ◽  
Mimo System ◽  
Ergodic Capacity ◽  
Base Station ◽  
Simulation Tool ◽  
Mobile System ◽  
Source Power ◽  
Mobile Stations

In this research the performance of 5G mobile system is evaluated through the Ergodic capacity metric. Today, in an­­y wireless communication system, many parameters have a significant role on system performance. Three main parameters are of concern here; the source power, number of antennas, and transmitter-receiver distance. User equipment’s (UEs) with equal and non-equal powers are used to evaluate the system performance in addition to using different antenna techniques to demonstrate the differences between SISO, MIMO, and massive MIMO. Using two mobile stations (MS) with different distances from the base station (BS), resulted in showing how using massive MIMO system will improve the performance than the standard SISO and MIMO techniques, under Rayleigh fading channel. Using MATLAB as a simulation tool it was found that the ergodic channel capacity enhance by increasing the power of the source and the base station antenna and it behaves in an opposite way with distance.

scholarly journals Dynamic Beamforming for Three-Dimensional MIMO Technique in LTE-Advanced Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Li ◽  
Xiaodong Ji ◽  
Dong Liang ◽  
Yuan Li
Keyword(s):  
Massive Mimo ◽  
Mimo System ◽  
Three Dimensional ◽  
System Level ◽  
System Level Simulation ◽  
Cell Coverage ◽  
Mimo Networks ◽  
Lte Advanced ◽  
3D Mimo ◽  
Beamforming Algorithm

MIMO system with large number of antennas, referred to as large MIMO or massive MIMO, has drawn increased attention as they enable significant throughput and coverage improvement in LTE-Advanced networks. However, deploying huge number of antennas in both transmitters and receivers was a great challenge in the past few years. Three-dimensional MIMO (3D MIMO) is introduced as a promising technique in massive MIMO networks to enhance the cellular performance by deploying antenna elements in both horizontal and vertical dimensions. Radio propagation of user equipments (UE) is considered only in horizontal domain by applying 2D beamforming. In this paper, a dynamic beamforming algorithm is proposed where vertical domain of antenna is fully considered and beamforming vector can be obtained according to UEs’ horizontal and vertical directions. Compared with the conventional 2D beamforming algorithm, throughput of cell edge UEs and cell center UEs can be improved by the proposed algorithm. System level simulation is performed to evaluate the proposed algorithm. In addition, the impacts of downtilt and intersite distance (ISD) on spectral efficiency and cell coverage are explored.

Sign in / Sign up

Export Citation Format

Share Document