scholarly journals A Triply Selective MIMO Channel Simulator Using GPUs

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
R. Carrasco-Alvarez ◽  
R. Carreón-Villal ◽  
J. Vázquez Castillo ◽  
J. Ortegón Aguilar ◽  
O. Longoria-Gandara ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
Multiple Input Multiple Output ◽  
Direct Consequence ◽  
Mimo Channel ◽  
Computational Time ◽  
Space Correlation ◽  
Single Input Single Output ◽  
Single Output ◽  
Input Multiple Output ◽  
Channel Simulator

A methodology for implementing a triply selective multiple-input multiple-output (MIMO) simulator based on graphics processing units (GPUs) is presented. The resulting simulator is based on the implementation of multiple double-selective single-input single-output (SISO) channel generators, where the multiple inputs and the multiple received signals have been transformed in order to supply the corresponding space correlation of the channel under consideration. A direct consequence of this approach is the flexibility provided, which allows different propagation statistics to each SISO channel to be specified and thus more complex environments to be replicated. It is shown that under some specific constraints, the statistics of the triply selective MIMO simulator are the same as those reported in the state of art. Simulation results show the computational time improvement achieved, up to 650-fold for an 8 × 8 MIMO channel simulator when compared with sequential implementations. In addition to the computational improvement, the proposed simulator offers flexibility for testing a variety of scenarios in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems.

A Design Method for Stabilizing Modified Smith Predictors for Multiple-Input/Multiple-Output Time-Delay Plants

2010 ◽  
Vol 459 ◽  
pp. 221-233 ◽  
Author(s):  
Kou Yamada ◽  
Nghia Thi Mai ◽  
Yoshinori Ando ◽  
Takaaki Hagiwara ◽  
Iwanori Murakami ◽  
...  
Keyword(s):  
Time Delay ◽  
Design Method ◽  
Multiple Input Multiple Output ◽  
Smith Predictor ◽  
Single Input Single Output ◽  
Single Output ◽  
Multiple Input ◽  
Modified Smith Predictor ◽  
Input Multiple Output ◽  
Output Time

The modified Smith predictor is well known as an effective time-delay compensator for a plant with large time-delays, and several papers on the modified Smith predictor have been published. The parameterization of all stabilizing modified Smith predictors for single-input/single-output time-delay plants is obtained by Yamada et al. However, they do not examine the parameterization of all stabilizing modified Smith predictors for multiple-input/multiple-output time-delay plants. The purpose of this paper is to expand the result by Yamada et al. and to propose the parameterization of all stabilizing modified Smith predictors for multiple-input/multiple-output time-delay plants. Control characteristics of the control system using obtained parameterization of all stabilizing modified Smith predictors are also given. Finally, a numerical example is illustrated to show the effectiveness of proposed parameterization of all stabilizing modified Smith predictors.

scholarly journals A Semiempirical MIMO Channel Model in Obstructed Viaduct Scenarios on High-Speed Railway

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Zhou ◽  
Cheng Tao ◽  
Liu Liu ◽  
Zhenhui Tan
Keyword(s):  
High Speed ◽  
Channel Model ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Time Correlation ◽  
Time Correlation Function ◽  
Mimo Channel ◽  
High Speed Railway ◽  
Single Input Single Output ◽  
Single Output

A semiempirical multiple-input multiple-output (MIMO) channel model is proposed for high-speed railway (HSR) viaduct scenarios. The proposed MIMO model is based on the combination of realistic single-input single-output (SISO) channel measurement results and a theoretical geometry-based stochastic model (GBSM). Temporal fading characteristics involvingK-factor and Doppler power spectral density (PSD) are derived from the wideband measurement under an obstructed viaduct on Zhengzhou-Xi’an HSR in China. The GBSM composed of a one-ring model and an elliptical model is employed to describe the entire propagation environment. Environment-related parameters in the GBSM are determined by the measured temporal fading properties. And a close agreement is achieved between the model results and measured data. Finally, a deterministic simulation model is established to perform the analysis of the space-time correlation function, the space-Doppler PSD, and the channel capacity for the measured scenario. This model is more realistic and particularly beneficial for the performance evaluation of MIMO systems in HSR environments.

Multi-Input Multi-Output Modal Testing Techniques for a Gossamer Structure

2002 ◽  
Author(s):  
Eric Ruggiero ◽  
Gyuhae Park ◽  
Daniel J. Inman ◽  
Jan Wright
Keyword(s):  
Fiber Composite ◽  
Multiple Input Multiple Output ◽  
Modal Testing ◽  
Single Input Single Output ◽  
Single Output ◽  
Input Multiple Output ◽  
Global Modes ◽  
Testing Techniques ◽  
Multiple Actuators ◽  
Langley Research Center

Inflated space-based structures have become popular over the past three decades due to their minimal launch-mass and launch-volume. Once inflated, these space structures are subject to vibrations induced by guidance systems and space debris as well as from variable amounts of direct sunlight. Understanding the dynamic behavior of space-based structures is critical to ensuring their desired performance. Inflated materials, however, pose special problems when testing and trying to control their vibrations because of their lightweight, flexibility, and high damping. Traditional modal testing techniques, based on single-input, single-output (SISO) methods, are limited for a variety of reasons when compared to their multiple counterparts. More specifically, SISO modal testing techniques are unable to reliably distinguish between pairs of modes that are inherent to axi-symmetric structures (such as an inflated torus, a critical component of a gossamer spacecraft). Furthermore, it is questionable as to whether a single actuator could reliably excite the global modes of a true gossamer craft, such as a 25 m diameter torus. In this study, we demonstrate the feasibility of using a multiple-input multiple-output (MIMO) modal testing technique on an inflated torus. In particular, the refined modal testing methodology focuses on using Macro-Fiber Composite (MFC®) patches (from NASA Langley Research Center) as both actuators and sensors. MFC® patches can be integrated in an unobtrusive way into the skin of the torus, and can be used to find a gossamer structure’s modal parameters. Furthermore, MFC® excitation produces less interference with suspension modes of the free-free torus than excitations from a conventional shaker. The use of multiple actuators is shown to properly excite the global modes of the structure and distinguish between pairs of modes at nearly identical resonant frequencies. Formulation of the MIMO test as well as the required postprocessing techniques are explained and successfully applied to an inflated Kapton® torus.

scholarly journals A new Transmission and Reception Algorithms for Improving the Performance of SISO/MIMO- OFDM Wireless Communication System

2021 ◽  
Vol 28 (3) ◽  
pp. 146-158
Author(s):  
Maha Monther Shahab ◽  
Saad Mshhain Hardan ◽  
Asmaa Salih Hammoodi
Keyword(s):  
Wireless Communication ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Mimo Channels ◽  
Single Input Single Output ◽  
Single Output ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm ◽  
Single Input

The future wireless communication requires a reliable transmission at high data rates, so the transmission over frequency-selective fading Multiple-Input–Multiple-Output MIMO channels become interesting since the capacity of "MIMO" channels expressions enormous gains above that of their essential single-input–single-output "SISO" channels. This paper examines the performance of the Low Complexity Zero Forcing "LCZF" equalizer for both systems single-input–single-output-Orthogonal Frequency Division Multiplexing" SISO-OFDM" and spatially multiplexed-Multiple-Input–Multiple-Output "SM-MIMO-OFDM" with different "QAM" modulations. It is exploring a new algorithm to improve the performance of the "BER", spectral efficiency, and power efficiency and to reduce the complexity of the "RF" communication system under the effect of the Additive White Gaussian Noise "AWGN" and multipath fading channel. It is also improves an efficient channel by developing a Low Complexity Zero Forcing "LCZF" equalizer for both "SISO-OFDM" and "SM-MIMO-OFDM" wireless Communication systems. This is done by proposing a new algorithm at the receiver side to covert the Linear Convolution in to Cyclic Convolution by adding Zero Padding "ZP" to the channel impulse response in such a way to be the same length to the transmitted signal in the time domain which is of length N, where N is the length of "IFFT".

scholarly journals The BER Analysis of MIMO System for M-PSK over Different Fading Channels using STBC Code Structure

2019 ◽  
Vol 8 (3) ◽  
pp. 5831-5836
Keyword(s):  
Fading Channels ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Spatial Diversity ◽  
Single Input Single Output ◽  
Single Output ◽  
Code Structure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Rician Channel

High information rates inside the restricted frequency (RF) spectrum is often fascinating that results in radios with capabilities on the far side a single-input single-output (SISO) topology. In recent days introduced wireless systems have adopted multiple-input multiple-output (MIMO) topologies that use 2 or more transmitters and 2 or more receivers to send information at the same time over same RF bandwidth. The performance of MIMO system may be improved by involving multiple antennas at transmitter and receiver therefore on offer spatial diversity. during this paper, the performance analysis of MIMO system over AWGN attenuation channel and Rician Channel with ZF receiver is bestowed. The consequences of the antenna choice can even be analyzed from the simulated results. The BER (Bit Error Rate) performance characteristics of ZeroForcing (ZF) receiver is investigated for M-PSK modulation technique over the AWGN channel and Rician Channel.

scholarly journals Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems

2020 ◽  
Author(s):  
Yu Wang ◽  
Juan Wang ◽  
Jie Yang ◽  
Wei Zhang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Decision Rules ◽  
Modulation Classification ◽  
Single Input Single Output ◽  
Automatic Modulation Classification ◽  
Single Output ◽  
Input Multiple Output

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

scholarly journals Optimized Scheduling Technique of Null Subcarriers for Peak Power Control in 3GPP LTE Downlink

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Soobum Cho ◽  
Sang Kyu Park
Keyword(s):  
Power Efficiency ◽  
Multiple Access ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Papr Reduction ◽  
Single Input Single Output ◽  
Single Output ◽  
Multiple Input ◽  
Input Multiple Output

Orthogonal frequency division multiple access (OFDMA) is a key multiple access technique for the long term evolution (LTE) downlink. However, high peak-to-average power ratio (PAPR) can cause the degradation of power efficiency. The well-known PAPR reduction technique, dummy sequence insertion (DSI), can be a realistic solution because of its structural simplicity. However, the large usage of subcarriers for the dummy sequences may decrease the transmitted data rate in the DSI scheme. In this paper, a novel DSI scheme is applied to the LTE system. Firstly, we obtain the null subcarriers in single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, respectively; then, optimized dummy sequences are inserted into the obtained null subcarrier. Simulation results show that Walsh-Hadamard transform (WHT) sequence is the best for the dummy sequence and the ratio of 16 to 20 for the WHT and randomly generated sequences has the maximum PAPR reduction performance. The number of near optimal iteration is derived to prevent exhausted iterations. It is also shown that there is no bit error rate (BER) degradation with the proposed technique in LTE downlink system.

scholarly journals Linear detectors and precoding methods for massive MIMO

2021 ◽  
Vol 42 (2) ◽  
pp. 209
Author(s):  
Jean Marcel Faria Tonin ◽  
Taufik Abrao
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Wireless Channel ◽  
Single Input Single Output ◽  
Single Output ◽  
Minimum Mean Squared Error ◽  
Input Multiple Output

Detection in multiple-input-multiple-output (MIMO) wireless communication systems is a crucial procedure in receivers since the multiple access transmission schemes generate interference due to the simultaneous transmission along with the several antennas, unlike single-input-single-output (SISO) transmission schemes. Precoding is a technique in MIMO systems used to mitigate the effects of the channel over the received signal. Hence, it is possible to adjust continuously the transmitted information to reverse the effect of the wireless channel at the receiver side. In this work, linear sub-optimal detectors and precoders for massive MIMO (M-MIMO) systems are implemented, analyzed, and compared in terms of performance-complexity trade-off. It is also being considered numerical results in both channel scenarios: a) receiver and transmitter have perfect channel state information (CSI); b) complex channel coefficients are estimated with different levels of inaccuracy. Monte-Carlo simulations (MCS) reveal that linear zero-forcing (ZF) and minimum mean squared error (MMSE) massive MIMO detectors result in a certain robustness against multi-user interference when operating under low and medium system loading, L = K/M, thanks to the favourable propagation phenomenon arising in massive MIMO systems.

scholarly journals Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems

2020 ◽  
Author(s):  
Yu Wang ◽  
Juan Wang ◽  
Jie Yang ◽  
Wei Zhang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Decision Rules ◽  
Modulation Classification ◽  
Single Input Single Output ◽  
Automatic Modulation Classification ◽  
Single Output ◽  
Input Multiple Output

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

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