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 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 Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements

2020 ◽  
Vol 10 (21) ◽  
pp. 7492
Author(s):  
Daniel Fernandes ◽  
Francisco Cercas ◽  
Rui Dinis ◽  
Pedro Sebastião
Keyword(s):  
Multiple Input Multiple Output ◽  
Decision Feedback ◽  
Signal Quality ◽  
Coverage Area ◽  
Propagation Models ◽  
Single Input Single Output ◽  
Single Carrier ◽  
Single Output ◽  
The Impact ◽  
Siso Systems

The demand for ubiquitous telecommunications services forces operators to have a special concern about signal quality and the coverage area they offer to their customers. This was usually checked by using suitable propagation models for Single Input Single Output (SISO) systems, which are no longer the case for new and future mobile generations, such as 5G and beyond. To guarantee good signal quality coverage, operators started to replace these models with Multiple Input Multiple Output (MIMO) ones. To achieve the best results, these models are usually calibrated with Drive Test (DT) measures; however, the DTs available for MIMO propagation models are sparse, in contrast to SISO ones. The main contribution presented in this paper is a methodology to extend the propagation models of SISO systems so they can be applied in MIMO sytems with Single-Carrier and Frequency-Domain Equalization (SC-FDE), while still using DTs acquired for SISO systems. This paper presents the impact on Bit Error Rate (BER) performance and its coverage area resulting from the application of our proposed method. We consider a MIMO SC-FDE system with an Iterative Block Decision Feedback Equalization (IB-DFE) receiver and we present the improvement expressions for the BER that we illustrate with some simulations.

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 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 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.

Comparative analysis of SISO and wavelength diversity-based FSO systems at different transmitter power levels

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Varun Srivastava ◽  
Abhilash Mandloi ◽  
Dhiraj Kumar Patel
Keyword(s):  
Optical Signal ◽  
Free Space Optical ◽  
Optical Source ◽  
Transmit Power ◽  
Single Input Single Output ◽  
Transmitter Power ◽  
Single Output ◽  
Communication Links ◽  
Single Input ◽  
Matching Performance

AbstractFree space optical (FSO) communication refers to a line of sight technology, which comprises optical source and detector to create a link without the use of physical connections. Similar to other wireless communication links, these are severely affected by losses that emerged due to atmospheric turbulence and lead to deteriorated intensity of the optical signal at the receiver. This impairment can be compensated easily by enhancing the transmitter power. However, increasing the transmitter power has some limitations as per radiation regulations. The requirement of high transmit power can be reduced by employing diversity methods. This paper presents, a wavelength-based diversity method with equal gain combining receiver, an effective technique to provide matching performance to single input single output at a comparatively low transmit power.

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.

scholarly journals Fog and rain attenuation characterization and performance of terrestrial free space optical communication in Akure, Nigeria

2020 ◽  
Vol 4 (3) ◽  
pp. 125-134
Author(s):  
Ajewole M. O ◽  
Owolawi P. A ◽  
Ojo J. S ◽  
Adetunji R. M.
Keyword(s):  
Free Space ◽  
Communication Systems ◽  
Outer Space ◽  
Rain Attenuation ◽  
Integration Time ◽  
Free Space Optical ◽  
High Data ◽  
Space Optical Communication ◽  
And Performance ◽  
One Year

Reliable broadband communication requires secure high data rate and bandwidth links. With the observedincrease in broadband users, known communication systems such as RF and microwave links cannot promise suchrequirements due to link interference and low bandwidth. A current communication system that promises suchrequirements and more is Free Space Optical (FSO) communication. This system basically involves the transmissionof signal-modulated optical radiation from a transmitter to a receiver through the atmosphere or outer space. However,location-variant atmospheric channel degrades the performance of an FSO system under severe atmosphericconditions, thus necessitating local atmospheric attenuation studies.This paper presents the characterization of both fog- and rain-induced attenuation and the performance ofan FSO system in a terrestrial terrain at Akure, Nigeria. One-year archived visibility data and in-situ measured 1-minute integration time rain rate data obtained from Nigerian Meteorological Agency (NIMET) and the Departmentof Physics, Federal University of Technology, Akure were used to compute the fog- and rain-induced specificattenuations using Kruse model and Carboneur model respectively. The performance of the FSO system is analyzedthrough link margin by using the parameters of a commercial optical transceiver, Terescope 5000.

Sign in / Sign up

Export Citation Format

Share Document