Optimized Phase Noise Compensation Technique using Neural Network

Malik, Shamsher; Kumar, Suresh

doi:10.17485/ijst/2017/v10i5/104348

Cited by 11 publications

(16 citation statements)

References 18 publications

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“…The vast majority of known intelligent DMSS use methods of multi-criteria fuzzy alternatives evaluation. However, the analysis of works [1,[3][4][5][6][7][8][9][10][11][12][13] showed that in the vast majority of these methods have the following common limitations:…”

Section: Materials and Methods Of Researchmentioning

confidence: 99%

Development of a method for assessment and forecasting of the radio electronic environment

et al. 2021

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Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment. Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing

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Section: Materials and Methods Of Researchmentioning

confidence: 99%

Development of a method for assessment and forecasting of the radio electronic environment

et al. 2021

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“…However, even with all the advantages of neuro-fuzzy expert, they unfortunately have certain disadvantages. Here are the main ones [6][7][8][9]:…”

Section: Mathematical Formulation Of the Problem Of Analysis Of The Ementioning

confidence: 99%

Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

Pievtsov

Turinskyi

Zhyvotovskyi

et al. 2020

EUREKA: Physics and Engineering

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Nowadays, artificial intelligence has entered into all spheres of our life. The system of analysis of the electronic environment is not an exception. However, there are a number of problems in the analysis of the electronic environment, namely the signals. They are analyzed in a complex electronic environment against the background of intentional and natural interference. Also, the input signals do not match the standards due to the influence of different types of interference. Interpretation of signals depends on the experience of the operator, the completeness of additional information on a specific condition of uncertainty. The best solution in this situation is to integrate with the data of the information system analysis of the electronic environment and artificial neural networks. Their advantage is also the ability to work in real time and quick adaptation to specific situations. These circumstances cause uncertainty in the conditions of the task of signal recognition and fuzzy statements in their interpretation, when the additional involved information may be incomplete and the operator makes decisions based on their experience. That is why, in this article, an improved method for finding solutions for neuro-fuzzy expert systems of analysis of the electronic environment is developed. Improving the efficiency of information processing (reducing the error) of evaluation is achieved through the use of neuro-fuzzy artificial neural networks that are evolving and learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. High efficiency of information processing is also achieved through training in the architecture of artificial neural networks by taking into account the type of uncertainty of the information that has to be assessed and work with clear and fuzzy products. This reduces the computational complexity of decision-making and absence of accumulation of an error of training of artificial neural networks as a result of processing of the arriving information on an input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the electronic environment. This example showed an increase in the efficiency of assessment at the level of 20–25 % on the efficiency of the processing information

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“…A method for compensating phase noise using neural networks and its application for MIMO systems were developed in [21]. Phase noise of the channel and the bit error probability are assessed by this method.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…-combined pulse and frequency response of the channel state and the bit error probability are not used in estimation [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Method of integral estimation of channel state in the multiantenna radio communication systems

Kalantaievska

Pievtsov

Kuvshynov

et al. 2018

EEJET

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Optimized Phase Noise Compensation Technique using Neural Network

Cited by 11 publications

References 18 publications

Development of a method for assessment and forecasting of the radio electronic environment

Development of a method for assessment and forecasting of the radio electronic environment

Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

Method of integral estimation of channel state in the multiantenna radio communication systems

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