scholarly journals Capacity Analysis of Multiple-input-multiple-output System Over Rayleigh and Rician Fading Channel

2019 ◽  
Vol 3 (2) ◽  
pp. 70-74
Author(s):  
Yazen Saifuldeen Mahmood ◽  
Ghassan Amanuel Qasmarrogy
Keyword(s):  
Channel Capacity ◽  
Transmission Rate ◽  
Mimo System ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Ergodic Capacity ◽  
Rician Fading ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Water Filling Algorithm

This paper aims to analyze the channel capacity in terms of spectral efficiency of a multiple-input-multiple-output (MIMO) system when channel state information (CSI) is known using water-filling algorithm and unknown at the transmitter side which it has been shown that the knowledge of the CSI at the transmitter enhancing the performance, the random Rayleigh and Rician channel models are assumed. Ergodic capacity and outage probability are the most channel capacity definitions which are investigated in this study. MATLAB code is devised to simulate the capacity of MIMO system for different numbers of antenna nodes versus different signal-to-noise ratio (SNR) values. In addition, the outage capacity probabilities for vary transmission rate and SNR are discussed.

scholarly journals PENGARUH PENYUSUNAN POLARISASI PADA SISTEM ANTENA MIMO TERHADAP KAPASITAS KANAL

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Abdurrahman Rizki ◽  
Alloysius Adya Pramudita ◽  
Trasma Yunita
Keyword(s):  
Circular Polarization ◽  
Channel Capacity ◽  
Mimo System ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Antenna System ◽  
Mimo Antenna ◽  
Left Hand ◽  
Multiple Input ◽  
Input Multiple Output

Multiple Input Multiple Output (MIMO) system is a technology that has the potential to be developed to increase channel capacity. The increase in channel capacity in the MIMO system is not only determined by the number of antennas, but is determined by the characteristics and arrangement of the antenna concept. This study identifies the effect of circular polarization on the MIMO antenna system on channel capacity. Co-polarization consists of a Left Hand Circular Polarization (LHCP) and Right Hand Circular Polarization (RHCP) configuration, while cross-polarization consists of an RHCP-LHCP configuration. The co-polarization of the antenna with the LHCP configuration results in an estimated channel capacity of 11,578 bps / Hz when it is at the lowest Signal to Noise Ratio (SNR) is 5 dB

scholarly journals Effects of Path Visibility on Urban MIMO Systems

1970 ◽  
Vol 5 (1) ◽  
pp. 22-30
Author(s):  
Sirichai Hemrungrote ◽  
Toshikazu Hori ◽  
Mitoshi Fujimoto ◽  
Kentaro Nishimori
Keyword(s):  
Channel Capacity ◽  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Propagation Characteristics ◽  
Simple Method ◽  
Limited Bandwidth ◽  
Multiple Input ◽  
Input Multiple Output

Multiple-Input Multiple-Output (MIMO) wireless communication technology is expected to improve the channel capacity over the limited bandwidth of existing networks. Since urban MIMO systems have complex propagation characteristics, the channel capacity cannot be estimated using a simple method. Hence, we introduce channel capacity characteristics to urban MIMO systems by using a combination of imaging and ray-launching methods as a ray-tracing scheme. A simulation based on these methods with variable parameters can reproducibly estimate various urban propagation characteristics and discriminate the effects of the urban model and antenna configurations. The characteristics of the Signal-to-Noise Ratio (SNR), the channel capacity, the spatial correlation, as well as the path visibility are then determined from the results of the simulation. The parameter called path visibility introduced in our previous study is considered again herein. We clarify that only this single parameter can be used to determine the channel capacity characteristics in urban MIMO scenarios. This parameter also provides guidance in determining the appropriate range for the base station (BS) height.

scholarly journals A Tradeoff between Rich Multipath and High Receive Power in MIMO Capacity

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zimu Cheng ◽  
Binghao Chen ◽  
Zhangdui Zhong
Keyword(s):  
Channel Capacity ◽  
Channel Model ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Mimo Channel ◽  
Channel Condition ◽  
Multiple Input ◽  
Two Factors ◽  
Input Multiple Output ◽  
Interesting Discussion

A discussion about which of the two factors, rich multipath (in NLOS) or signal-to-noise ratio (SNR) (usually in LOS), affects the Multiple-Input Multiple-Output (MIMO) channel capacity more is presented in this paper. This interesting discussion is investigated by performing simulations using simple circle scatterer model and WINNER II channel model. And the simulation shows that these two factors behave differently as the channel condition varyies. When the scatterer number in channel is low, the high receive SNR is more important to capacity. The multipath richness will have greater influence when the scatterer number exceeds a certain threshold. However, the channel capacity will not change much as the scatterers continue to increase.

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.

scholarly journals Channel capacity and performance evaluation of precoded MIMO-OFDM system with large-size constellation

2019 ◽  
Vol 9 (6) ◽  
pp. 5024
Author(s):  
Hussein A. Leftah ◽  
Huda N. Alminshid
Keyword(s):  
Fading Channels ◽  
Channel Capacity ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Ofdm System ◽  
Large Size ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm ◽  
And Performance

<p>Multiple input-multiple output (MIMO) is a multipath diversity exploring approach which is emerged with orthogonal frequency division multiplexing (OFDM) to produce MIMO-OFDM that is widely used in wireless communications. This paper presents a discrete Hart-ley transform (DHT) precoded MIMO-OFDM system over multipath frequency-selective fading channel with large-size quadrature amplitude modulation (16-QAM, 64-QAM and 256-QAM). A mathematical models for the BER and channel capacity over mutlipath fading channels are also derived in this paper. Average Bit-error-rate (BER) and channel capacity of the presented system is considered and compared with that of the traditional MIMO-OFDM. Simulation results shows that the transmission performance and channel capacity of the proposed schemes is better than that of the traditional MIMO-OFDM without a pre-coder.</p>

Channel capacity evaluation for a multiple-input-multiple-output terminal in the presence of user's hand

2007 ◽  
Vol 1 (6) ◽  
pp. 1137 ◽  
Author(s):  
T. Zervos ◽  
K. Peppas ◽  
F. Lazarakis ◽  
A.A. Alexandridis ◽  
K. Dangakis ◽  
...  
Keyword(s):  
Channel Capacity ◽  
Multiple Input Multiple Output ◽  
Output Terminal ◽  
Capacity Evaluation ◽  
Multiple Input ◽  
Input Multiple Output

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.

scholarly journals Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems

2019 ◽  
Vol 9 (21) ◽  
pp. 4624
Author(s):  
Uzokboy Ummatov ◽  
Kyungchun Lee
Keyword(s):  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Threshold Value ◽  
Adaptive Threshold ◽  
Sphere Decoding ◽  
High Signal ◽  
Signal To Noise ◽  
Multiple Input ◽  
Input Multiple Output

This paper proposes an adaptive threshold-aided K-best sphere decoding (AKSD) algorithm for large multiple-input multiple-output systems. In the proposed scheme, to reduce the average number of visited nodes compared to the conventional K-best sphere decoding (KSD), the threshold for retaining the nodes is adaptively determined at each layer of the tree. Specifically, we calculate the adaptive threshold based on the signal-to-noise ratio and index of the layer. The ratio between the first and second smallest accumulated path metrics at each layer is also exploited to determine the threshold value. In each layer, in addition to the K paths associated with the smallest path metrics, we also retain the paths whose path metrics are within the threshold from the Kth smallest path metric. The simulation results show that the proposed AKSD provides nearly the same bit error rate performance as the conventional KSD scheme while achieving a significant reduction in the average number of visited nodes, especially at high signal-to-noise ratios.

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