PERFORMANCE EVALUATION OF TRANSMISSION FOR MULTIUSER MIMO BROADCAST CHANNEL

2016 ◽  
Vol 78 (5-7) ◽  
Author(s):  
Mohd Syarhan Idris ◽  
Nur Idora Abdul Razak ◽  
Azlina Idris ◽  
Ruhani Ab Rahman
Keyword(s):  
Mimo System ◽  
Multiple Input Multiple Output ◽  
System Capacity ◽  
Broadcast Channel ◽  
High Data ◽  
Dirty Paper Coding ◽  
Percentage Improvement ◽  
Data Rates ◽  
Multiple Input ◽  
Input Multiple Output

Multiple Input Multiple Output (MIMO) system has been brought a great improvement in spectral efficiency and the system capacity by serving multiple users simultaneously. The mathematical model of downlink Multi-user MIMO system and its capacity has been presented as well as different precoded transmission schemes. It is to implementing the downlink MU-MIMO system, such as channel inversion (CI), block diagonalization (BD), dirty paper coding (DPC) and tomlinsonharashimaprecoding (THP). It is because, in wireless and mobile communication system has been requires a reliable transmission of high data rates under various channel type different scenarios and reduce MU interference in the system.   These compares the method of transmission for broadcast channel (BC) and propose the best one method that outperforms existing technique with percentage improvement from the worst performance.

scholarly journals Capacity comparison of MIMO and cooperative MIMO

2017 ◽  
Vol 7 (1.1) ◽  
pp. 638
Author(s):  
Ashish Kumar Sarangi ◽  
Amrit Mukherjee ◽  
Amlan Datta
Keyword(s):  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
High Capacity ◽  
High Data ◽  
Cooperative Mimo ◽  
Main Requirement ◽  
Data Rates ◽  
Multiple Input ◽  
Input Multiple Output

To achieve high capacity and high data rates is the main requirement for today’s generation. This paper studies about the performance and capacity comparison of MIMO and cooperative MIMO systems. The comparison of capacity between multiple- input- multiple- output (MIMO) and cooperative MIMO systems helps us to know that which system have better performance and better capacity. The simulation results shows that among SISO, SIMO, MISO and MIMO  system the capacity of MIMO will be better but in between MIMO and cooperative MIMO, Cooperative MIMO system have high capacity than MIMO systems.  

scholarly journals 3D Multiple Input Multiple Output Large-Scale Antenna Systems

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Saif Saad Hameed ◽  
Fouad H. Awad ◽  
Adnan Yousif Dawod ◽  
Ayoob Abdulmunem Abdulhameed
Keyword(s):  
Large Scale ◽  
Random Noise ◽  
Multiple Input Multiple Output ◽  
Least Square ◽  
Noise Sources ◽  
Estimation Algorithms ◽  
High Data ◽  
Data Rates ◽  
Multiple Input ◽  
Input Multiple Output

The channel could be evaluated by utilizing several estimation algorithms. The various patterns of pilot arrangements for the channel appreciation are a huge problem in channel appreciation techniques since all the processes depends on it; this paper discusses improvements in channel selection. The Least Square and Least Square Mean methods are common, simple ways to begin to estimate a channel; however, they are less efficient than more complex approaches. Due to the boost in demand with high data rates in communications, developers continue to invent new methods and mechanisms to adjust the capacity and the accuracy of the communication network. One of the primary troubles in wireless communication is the communication channel, which is affected by nonlinear and random noise sources, which decrease the quality of the service on the network; in this case, the channel must be equalized to increase performance with minimal error. In this paper, a Massive Multiple Input Multiple Output was designed and simulated in order to estimate the channel and the performance of the network through using Least Square and Least Square Mean.

scholarly journals A Fast Adaptive Receive Antenna Selection Method in MIMO System

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Chaowei Wang ◽  
Weidong Wang ◽  
Cheng Wang ◽  
Shuai Wang ◽  
Yang Yu
Keyword(s):  
Mimo Systems ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
System Capacity ◽  
Adaptive Antenna ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Hardware Costs ◽  
Receive Antenna

Antenna selection has been regarded as an effective method to acquire the diversity benefits of multiple antennas while potentially reduce hardware costs. This paper focuses on receive antenna selection. According to the proportion between the numbers of total receive antennas and selected antennas and the influence of each antenna on system capacity, we propose a fast adaptive antenna selection algorithm for wireless multiple-input multiple-output (MIMO) systems. Mathematical analysis and numerical results show that our algorithm significantly reduces the computational complexity and memory requirement and achieves considerable system capacity gain compared with the optimal selection technique in the same time.

Effect of Antenna Layout on the Capacity of Massive MIMO

2014 ◽  
Vol 716-717 ◽  
pp. 1194-1198
Author(s):  
Xiao Yu Li ◽  
Xiao Fei Zhang ◽  
Da Zhuan Xu ◽  
Qui Ming Zhu
Keyword(s):  
Channel Model ◽  
Narrow Band ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
System Capacity ◽  
Antenna Aperture ◽  
Multiple Input ◽  
Flat Fading ◽  
Input Multiple Output ◽  
Capacity Performance

This paper addresses the problem of the effect of different antenna layouts on the capacity of massive multiple-input multiple-output (MIMO) system capacity. Based on the narrow-band, flat fading channel model, the effect of scattering environment and antenna layout are considered by incorporating the power azimuth spectrum (PAS) and the array manifold vector. Under the same antenna aperture, six antenna layouts are investigated among which the UCA yields the best capacity while ULA yields the lowest capacity. The more symmetric the antenna geometry is, the better capacity performance it has.

scholarly journals mmWave massive analog relay MIMO

2021 ◽  
Vol 2 (6) ◽  
pp. 43-55
Author(s):  
Kei Sakaguchi ◽  
Takumi Yoneda ◽  
Masashi Iwabuchi ◽  
Tomoki Murakami
Keyword(s):  
Communication Systems ◽  
Vehicular Networks ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Path Loss ◽  
Relay Nodes ◽  
High Data ◽  
Data Rates ◽  
Input Multiple Output ◽  
4G Lte

Millimeter-Wave (mmWave) communications are a key technology to realize ultra-high data rate and ultra-low latency wireless communications. Compared with conventional communication systems in the microwave band such as 4G/LTE, mmWave communications employ a higher frequency band which allows a wider bandwidth and is suitable for large capacity communications. It is expected to be applied to various use cases such as mmWave cellular networks and vehicular networks. However, due to the strong diffraction loss and the path loss in the mmWave band, it is difficult or even impossible to achieve high channel capacity for User Equipment (UE) located in Non-Line-Of-Sight (NLOS) environments. To solve the problem, the deployment of relay nodes has been considered. In this paper, we consider the use of massive analog Relay Stations (RSs) to relay the transmission signals. By relaying the signals by a large number of RSs, an artificial Multiple-Input Multiple-Output (MIMO) propagation environment can be formed, which enables mmWave MIMO communications to the NLOS environment. We describe a theoretical study of a massive relay MIMO system and extend it to include multi-hop relays. Simulations are conducted, and the numerical results show that the proposed system achieves high data rates even in a grid-like urban environment.

A companding approach for PAPR suppression in OFDM based massive MIMO system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Kumar Yadav ◽  
Pritam Keshari Sahoo ◽  
Yogendra Kumar Prajapati
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Base Station ◽  
Convex Optimization Problem ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm

Abstract Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results.

scholarly journals Energy and Information Beamforming in Airborne Massive MIMO System for Wireless Powered Communications

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3540 ◽  
Author(s):  
Yurong Wang ◽  
Aijun Liu ◽  
Kui Xu ◽  
Xiaochen Xia
Keyword(s):  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Hardware Complexity ◽  
Mimo Channels ◽  
Channel State ◽  
Active Antennas ◽  
Large Numbers ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Critical Problems

Energy supply and information backhaul are critical problems for wireless sensor networks deployed in remote places with poor infrastructure. To deal with these problems, this paper proposes an airborne massive multiple-input multiple-output (MIMO) system for wireless energy transfer (WET) and information transmission. An air platform (AP) equipped with a two-dimensional rectangular antenna array is employed to broadcast energy and provide wireless access for ground sensors. By exploiting the statistical property of air-terrestrial MIMO channels, the energy and information beamformers are jointly designed to maximize the average received signal-to-interference-plus-noise ratio (SINR), which gives rise to a statistical max-SINR beamforming scheme. The scheme does not rely on the instantaneous channel state information, but still requires large numbers of RF chains at AP. To deal with this problem, a heuristic strongest-path energy and information beamforming scheme is proposed, which can be implemented in the analog-domain with low computational and hardware complexity. The analysis of the relation between the two schemes reveals that, with proper sensor scheduling, the strongest-path beamforming is equivalent to the statistical max-SINR beamforming when the number of AP antennas tends to infinity. Using the asymptotic approximation of average received SINR at AP, the system parameters, including transmit power, number of active antennas of AP and duration of WET phase, are optimized jointly to maximize the system energy efficiency. The simulation results demonstrate that the proposed schemes achieve a good tradeoff between system performance and complexity.

Precoding for Multiuser MIMO

2012 ◽  
pp. 130-156 ◽  
Author(s):  
Elsadig Saeid ◽  
Varun Jeoti ◽  
Brahim Belhaouari Samir
Keyword(s):  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Achievable Rate ◽  
Multiple Access Channels ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Single User ◽  
A New Technique ◽  
Improved Technique

Future Wireless Networks are expected to adopt multi-user multiple input multiple output (MU-MIMO) systems whose performance is maximized by making use of precoding at the transmitter. This chapter describes the recent advances in precoding design for MU-MIMO and introduces a new technique to improve the precoder performance. Without claiming to be comprehensive, the chapter gives deep introduction on basic MIMO techniques covering the basics of single user multiple input multiple output (SU-MIMO) links, its capacity, various transmission strategies, SU-MIMO link precoding, and MIMO receiver structures. After the introduction, MU-MIMO system model is defined and maximum achievable rate regions for both MU-MIMO broadcast and MU-MIMO multiple access channels are explained. It is followed by critical literature review on linear precoding design for MU-MIMO broadcast channel. This paves the way for introducing an improved technique of precoding design that is followed by its performance evaluation.

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