Cargando…

A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm

In wireless communication, multiple signals are utilized to receive and send information in the form of signals simultaneously. These signals consume little power and are usually inexpensive, with a high data rate during data transmission. An Multi Input Multi Output (MIMO) system uses numerous ante...

Descripción completa

Detalles Bibliográficos
Autores principales: Manasa, B. M. R., Pakala, Venugopal, Chinthaginjala, Ravikumar, Ayadi, Manel, Hamdi, Monia, Ksibi, Amel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675390/
https://www.ncbi.nlm.nih.gov/pubmed/38005540
http://dx.doi.org/10.3390/s23229154
_version_ 1785149813915910144
author Manasa, B. M. R.
Pakala, Venugopal
Chinthaginjala, Ravikumar
Ayadi, Manel
Hamdi, Monia
Ksibi, Amel
author_facet Manasa, B. M. R.
Pakala, Venugopal
Chinthaginjala, Ravikumar
Ayadi, Manel
Hamdi, Monia
Ksibi, Amel
author_sort Manasa, B. M. R.
collection PubMed
description In wireless communication, multiple signals are utilized to receive and send information in the form of signals simultaneously. These signals consume little power and are usually inexpensive, with a high data rate during data transmission. An Multi Input Multi Output (MIMO) system uses numerous antennas to enhance the functionality of the system. Moreover, system intricacy and power utilization are difficult and highly complicated tasks to achieve in an Analog to Digital Converter (ADC) at the receiver side. An infinite number of MIMO channels are used in wireless networks to improve efficiency with Cross Entropy Optimization (CEO). ADC is a serious issue because the data of the accepted signal are completely lost. ADC is used in the MIMO channels to overcome the above issues, but it is very hard to implement and design. So, an efficient way to enhance the estimation of channels in the MIMO system is proposed in this paper with the utilization of the heuristic-based optimization technique. The main task of the implemented channel prediction framework is to predict the channel coefficient of the MIMO system at the transmitter side based on the receiver side error ratio, which is obtained from feedback information using a Hybrid Serial Cascaded Network (HSCN). Then, this multi-scaled cascaded autoencoder is combined with Long Short Term Memory (LSTM) with an attention mechanism. The parameters in the developed Hybrid Serial Cascaded Multi-scale Autoencoder and Attention LSTM are optimized using the developed Hybrid Revised Position-based Wild Horse and Energy Valley Optimizer (RP-WHEVO) algorithm for minimizing the “Root Mean Square Error (RMSE), Bit Error Rate (BER) and Mean Square Error (MSE)” of the estimated channel. Various experiments were carried out to analyze the accomplishment of the developed MIMO model. It was visible from the tests that the developed model enhanced the convergence rate and prediction performance along with a reduction in the computational costs.
format Online
Article
Text
id pubmed-10675390
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-106753902023-11-13 A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm Manasa, B. M. R. Pakala, Venugopal Chinthaginjala, Ravikumar Ayadi, Manel Hamdi, Monia Ksibi, Amel Sensors (Basel) Article In wireless communication, multiple signals are utilized to receive and send information in the form of signals simultaneously. These signals consume little power and are usually inexpensive, with a high data rate during data transmission. An Multi Input Multi Output (MIMO) system uses numerous antennas to enhance the functionality of the system. Moreover, system intricacy and power utilization are difficult and highly complicated tasks to achieve in an Analog to Digital Converter (ADC) at the receiver side. An infinite number of MIMO channels are used in wireless networks to improve efficiency with Cross Entropy Optimization (CEO). ADC is a serious issue because the data of the accepted signal are completely lost. ADC is used in the MIMO channels to overcome the above issues, but it is very hard to implement and design. So, an efficient way to enhance the estimation of channels in the MIMO system is proposed in this paper with the utilization of the heuristic-based optimization technique. The main task of the implemented channel prediction framework is to predict the channel coefficient of the MIMO system at the transmitter side based on the receiver side error ratio, which is obtained from feedback information using a Hybrid Serial Cascaded Network (HSCN). Then, this multi-scaled cascaded autoencoder is combined with Long Short Term Memory (LSTM) with an attention mechanism. The parameters in the developed Hybrid Serial Cascaded Multi-scale Autoencoder and Attention LSTM are optimized using the developed Hybrid Revised Position-based Wild Horse and Energy Valley Optimizer (RP-WHEVO) algorithm for minimizing the “Root Mean Square Error (RMSE), Bit Error Rate (BER) and Mean Square Error (MSE)” of the estimated channel. Various experiments were carried out to analyze the accomplishment of the developed MIMO model. It was visible from the tests that the developed model enhanced the convergence rate and prediction performance along with a reduction in the computational costs. MDPI 2023-11-13 /pmc/articles/PMC10675390/ /pubmed/38005540 http://dx.doi.org/10.3390/s23229154 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Manasa, B. M. R.
Pakala, Venugopal
Chinthaginjala, Ravikumar
Ayadi, Manel
Hamdi, Monia
Ksibi, Amel
A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm
title A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm
title_full A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm
title_fullStr A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm
title_full_unstemmed A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm
title_short A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm
title_sort novel channel estimation framework in mimo using serial cascaded multiscale autoencoder and attention lstm with hybrid heuristic algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675390/
https://www.ncbi.nlm.nih.gov/pubmed/38005540
http://dx.doi.org/10.3390/s23229154
work_keys_str_mv AT manasabmr anovelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT pakalavenugopal anovelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT chinthaginjalaravikumar anovelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT ayadimanel anovelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT hamdimonia anovelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT ksibiamel anovelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT manasabmr novelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT pakalavenugopal novelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT chinthaginjalaravikumar novelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT ayadimanel novelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT hamdimonia novelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm
AT ksibiamel novelchannelestimationframeworkinmimousingserialcascadedmultiscaleautoencoderandattentionlstmwithhybridheuristicalgorithm