scholarly journals PERFORMANCE EVALUATION OF RELAYING SCHEMES FOR WIRELESS COMMUNICATION SYSTEMS

2013 ◽  
pp. 157-161
Author(s):  
MANISHA CRASTO BRAGANC ◽  
HASANALI G. VIRANI ◽  
SHAILESH KHANOLKAR
Keyword(s):  
Communication Systems ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Multiple Input Multiple Output ◽  
Amplify And Forward ◽  
Decode And Forward ◽  
Wireless System ◽  
Single Input Single Output ◽  
Single Output ◽  
Alamouti Coding

The evaluation of MIMO (multiple-input multiple-output) Relay wireless system is carried out and compared against the performance of a SISO (single-input single-output) Relay wireless system. The encoding scheme used in MIMO is Alamouti coding and decoding is done by the Maximum Likelihood (ML) detector. A comparison is made between the SISO non-regenerative amplify-and-forward (AF) and regenerative decode-and-forward (DF) relaying schemes. The plots of bit error rate (BER) versus signal to noise ratio (SNR) are simulated by incorporating Rayleigh fading condition in the presence of additive white Gaussian noise(AWGN) using MATLAB.

scholarly journals MMSE-NP-RISIC-Based Channel Equalization for MIMO-SC-FDE Troposcatter Communication Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Zedong Xie ◽  
Xihong Chen ◽  
Xiaopeng Liu ◽  
Yu Zhao
Keyword(s):  
Interference Cancellation ◽  
Communication Systems ◽  
Intersymbol Interference ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Channel Equalization ◽  
Single Input Single Output ◽  
Single Carrier ◽  
Single Output ◽  
The Impact

The impact of intersymbol interference (ISI) on single-carrier frequency-domain equalization with multiple input multiple output (MIMO-SC-FDE) troposcatter communication systems is severe. Most of the channel equalization methods fail to solve it completely. In this paper, given the disadvantages of the noise-predictive (NP) MMSE-based and the residual intersymbol interference cancellation (RISIC) equalization in the single input single output (SISO) system, we focus on the combination of both equalization schemes mentioned above. After extending both of them into MIMO system for the first time, we introduce a novel MMSE-NP-RISIC equalization method for MIMO-SC-FDE troposcatter communication systems. Analysis and simulation results validate the performance of the proposed method in time-varying frequency-selective troposcatter channel at an acceptable computational complexity cost.

scholarly journals Impact of pointing error on SISO/MISO drones swarm-based free space optical system in weak turbulence regime

2021 ◽  
Vol 23 (2) ◽  
pp. 918
Author(s):  
Abdullah Jameel Mahdi ◽  
Wamidh Jalil Mazher ◽  
Osman Nuri Ucan
Keyword(s):  
Free Space ◽  
Communication Systems ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Weather Conditions ◽  
Free Space Optical ◽  
Single Input Single Output ◽  
Pointing Error ◽  
Single Output ◽  
Link Distance

<p>Applying the drone-based free space optical (FSO) technology is recent in communication systems. The FSO technology hashigh-security features dueto narrow beamwidth, insusceptible to interferences, free license and landline connection is not appropriate. However, these advantages face many obstacles that affect the system's performance, such as random weather conditions and misalignment. The pointing error Hpis one of the critical factors of the channel gain H. The related parameters of the Hp factor: the pointing error angles θr and the path length Z, were manipulated to extract the applicable values at various receiver diameter values. The proposed system has two topologies: single input single output (SISO) and multiple input single output (MISO), flying in weak atmospheric turbulence. The simulation was done using MATLAB software 2020. The average bit error rate (ABER) for the system versus signal-to-noise ratio (SNR) were verified and analyzed. The results showed that at θr=10<sup>−3</sup>rad, Z increased in the range 10~100m for each one-centimeter increase of DR. At θr=10<sup>−2</sup>rad, the applicable Z was nearly 10% of the link distance Z when θr=10<sup>−3</sup>rad was applied. Consequently, an increase in θr must correspond decrease in Z and vice versa to maintain the system at high performance.</p>

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 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.

NDA SNR and CRLB Estimation Over MISO with STBC Channels

2012 ◽  
Vol 8 (4) ◽  
pp. 1-16
Author(s):  
Ahmed M. Almradi ◽  
Sohail A. Dianat
Keyword(s):  
Em Algorithm ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Single Output ◽  
Alamouti Coding ◽  
Single Input ◽  
Noise Ratio ◽  
Modulated Signals

This paper discusses the problem of Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation of Binary Phase Shift keying (BPSK) modulated signals using the Expectation Maximization (EM) Algorithm. In addition, the Cramer-Rao Lower Bounds (CRLB) for the estimation of Data Aided (DA) and Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation is derived. Multiple Input Single Output (MISO) channels with Space Time Block Codes (STBC) is used. The EM algorithm is a method that finds the Maximum Likelihood (ML) solution iteratively when there are unobserved (hidden or missing) data. Extension of the proposed approach to other types of linearly modulated signals in estimating SNR is straight forward. The performance of the estimator is assessed using the NDA CRLBs. Alamouti coding technique is used in this paper with two transmit antennas and one receive antenna. The authors’ assumption is that the received signal is corrupted by additive white Gaussian noise (AWGN) with unknown variance, and scaled by fixed unknown complex channel gain. Monte Carlo simulations are used to show that the proposed estimator offers a substantial improvement over the conventional Single Input Single Output (SISO) NDA SNR estimator due to the use of the statistical dependences in space and time. Moreover, the proposed NDA SNR estimator works close to the NDA SNR estimator over Single Input Multiple Output (SIMO) channels.

scholarly journals A Review on Biomedical MIMO Radars for Vital Sign Detection and Human Localization

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1497 ◽  
Author(s):  
Emanuele Cardillo ◽  
Alina Caddemi
Keyword(s):  
Vital Sign ◽  
Signal To Noise Ratio ◽  
Human Subjects ◽  
Multiple Input Multiple Output ◽  
Research Area ◽  
Single Input Single Output ◽  
Single Output ◽  
Research Activities ◽  
Sign Detection ◽  
Mimo Technology

This paper reports a thorough overview on the last developments concerning the vital sign detection and the human localization employing the multiple-input-multiple-output (MIMO) technology. The wireless motion and vital sign detection represents an outstanding research area aimed at monitoring the health conditions of human subjects and at detecting their presence in different environments with minimal concern. MIMO radars exhibit several interesting advantages over conventional single-input-single-output architectures mainly related to their angle detection capabilities and enhanced signal-to-noise ratio. This paper describes the main features and details the operating principles of MIMO technology. Thereafter, it summarizes the state-of-the-art of the available solutions with the purpose of fueling the research activities on this hot topic.

scholarly journals The Impact of Oil Well Fires on the Free Space Optical Systems

2019 ◽  
Vol 29 (3) ◽  
pp. 113 ◽  
Author(s):  
Firas S. Mohammed ◽  
Farouk K. Shaker
Keyword(s):  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Rain Attenuation ◽  
Optical Systems ◽  
Oil Well ◽  
Free Space Optical ◽  
Pollution Effects ◽  
Single Input Single Output ◽  
Single Output ◽  
The Impact

Aerosol particles in oil fire plumes caused crucial air pollution. The smoke plumes from the blazes initially launched 200-400 m into the air and then continued to rise. The presence of liquid and solid aerosols may cause severe disturbance to the propagation of optical and infrared waves, thus can produce harmful effects on the wireless communication systems. In this paper, we analyze the bit error rate (BER), single to noise ratio (SNR), Q- factor and outage performance of single-input single-output (SISO) and multiple-input multiple-output (MIMO) FSO systems under attenuation of dense smoke conditions. Obtained results demonstrated that the performance of (SISO) FSO link is degraded from the Fog, Smoke and acid-rain Attenuation due to their chemical nature, their size and their concentration. As well, (MIMO) FSO link is a highly efficient way can be minimal smoke pollution effects

scholarly journals Block Method for SolvingState-Space Equations of Linear Continuous-Time Control Systems

2007 ◽  
Vol 4 (2) ◽  
pp. 318-329
Author(s):  
Baghdad Science Journal
Keyword(s):  
State Space ◽  
Continuous Time ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Method Comparison ◽  
The State ◽  
Block Method ◽  
Single Input Single Output ◽  
Single Output ◽  
Time Control

This paper presents a newly developed method with new algorithms to find the numerical solution of nth-order state-space equations (SSE) of linear continuous-time control system by using block method. The algorithms have been written in Matlab language. The state-space equation is the modern representation to the analysis of continuous-time system. It was treated numerically to the single-input-single-output (SISO) systems as well as multiple-input-multiple-output (MIMO) systems by using fourth-order-six-steps block method. We show that it is possible to find the output values of the state-space method using block method. Comparison between the numerical and exact results has been given for some numerical examples for solving different types of state-space equations using block method for conciliated the accuracy of the results of this method.

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