Cargando…
Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
The limitation of the available channel bandwidth and availability of a sustainable energy source for battery feed sensor nodes are the main challenges in the underwater acoustic communication. Unlike terrestrial’s communication, using multi-input multi-output (MIMO) technologies to overcome the ban...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662830/ https://www.ncbi.nlm.nih.gov/pubmed/33126658 http://dx.doi.org/10.3390/s20216134 |
_version_ | 1783609487481896960 |
---|---|
author | Qasem, Zeyad A. H. Esmaiel, Hamada Sun, Haixin Qi, Jie Wang, Junfeng |
author_facet | Qasem, Zeyad A. H. Esmaiel, Hamada Sun, Haixin Qi, Jie Wang, Junfeng |
author_sort | Qasem, Zeyad A. H. |
collection | PubMed |
description | The limitation of the available channel bandwidth and availability of a sustainable energy source for battery feed sensor nodes are the main challenges in the underwater acoustic communication. Unlike terrestrial’s communication, using multi-input multi-output (MIMO) technologies to overcome the bandwidth limitation problem is highly restricted in underwater acoustic communication by high inter-channel interference (ICI) and the channel multipath effect. Recently, the spatial modulation techniques (SMTs) have been presented as an alternative solution to overcome these issues by transmitting more data bits using the spatial index of antennas transmission. This paper proposes a new scheme of SMT called spread-spectrum fully generalized spatial modulation (SS-FGSM) carrying the information bits not only using the constellated data symbols and index of active antennas as in conventional SMTs, but also transmitting the information bits by using the index of predefined spreading codes. Consequently, most of the information bits are transmitted in the index of the transmitter antenna, and the index of spreading codes. In the proposed scheme, only a few information bits are transmitted physically. By this way, consumed power transmission can be reduced, and we can save the energy of underwater nodes, as well as enhancing the channel utilization. To relax the receiver computational complexity, a low complexity deep learning (DL) detector is proposed for the SS-FGSM scheme as the first attempt in the underwater SMTs-based communication. The simulation results show that the proposed deep learning detector-based SS-FGSM (DLSS-FGSM), compared to the conventional SMTs, can significantly improve the system data rate, average bit error rate, energy efficiency, and receiver’s computational complexity. |
format | Online Article Text |
id | pubmed-7662830 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76628302020-11-14 Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication Qasem, Zeyad A. H. Esmaiel, Hamada Sun, Haixin Qi, Jie Wang, Junfeng Sensors (Basel) Article The limitation of the available channel bandwidth and availability of a sustainable energy source for battery feed sensor nodes are the main challenges in the underwater acoustic communication. Unlike terrestrial’s communication, using multi-input multi-output (MIMO) technologies to overcome the bandwidth limitation problem is highly restricted in underwater acoustic communication by high inter-channel interference (ICI) and the channel multipath effect. Recently, the spatial modulation techniques (SMTs) have been presented as an alternative solution to overcome these issues by transmitting more data bits using the spatial index of antennas transmission. This paper proposes a new scheme of SMT called spread-spectrum fully generalized spatial modulation (SS-FGSM) carrying the information bits not only using the constellated data symbols and index of active antennas as in conventional SMTs, but also transmitting the information bits by using the index of predefined spreading codes. Consequently, most of the information bits are transmitted in the index of the transmitter antenna, and the index of spreading codes. In the proposed scheme, only a few information bits are transmitted physically. By this way, consumed power transmission can be reduced, and we can save the energy of underwater nodes, as well as enhancing the channel utilization. To relax the receiver computational complexity, a low complexity deep learning (DL) detector is proposed for the SS-FGSM scheme as the first attempt in the underwater SMTs-based communication. The simulation results show that the proposed deep learning detector-based SS-FGSM (DLSS-FGSM), compared to the conventional SMTs, can significantly improve the system data rate, average bit error rate, energy efficiency, and receiver’s computational complexity. MDPI 2020-10-28 /pmc/articles/PMC7662830/ /pubmed/33126658 http://dx.doi.org/10.3390/s20216134 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Qasem, Zeyad A. H. Esmaiel, Hamada Sun, Haixin Qi, Jie Wang, Junfeng Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication |
title | Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication |
title_full | Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication |
title_fullStr | Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication |
title_full_unstemmed | Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication |
title_short | Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication |
title_sort | deep learning-based spread-spectrum fgsm for underwater communication |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662830/ https://www.ncbi.nlm.nih.gov/pubmed/33126658 http://dx.doi.org/10.3390/s20216134 |
work_keys_str_mv | AT qasemzeyadah deeplearningbasedspreadspectrumfgsmforunderwatercommunication AT esmaielhamada deeplearningbasedspreadspectrumfgsmforunderwatercommunication AT sunhaixin deeplearningbasedspreadspectrumfgsmforunderwatercommunication AT qijie deeplearningbasedspreadspectrumfgsmforunderwatercommunication AT wangjunfeng deeplearningbasedspreadspectrumfgsmforunderwatercommunication |