Channel State Information Prediction for Adaptive Underwater Acoustic Downlink OFDMA System: Deep Neural Networks Based Approach

Liu, Lei; Cai, Lin; Ma, Lu; Qiao, Gang

doi:10.1109/tvt.2021.3099797

Cited by 48 publications

(14 citation statements)

References 46 publications

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“…[34] have the satisfactory outcome produced during the design phase is further improved by the iterative supervised learning approach. From the above discussion, it proclaims that the present article provides higher spectral efficiency with reduced BER (0.002, 0.147, 0.0316, 0.0112), (0.050, 0.0316, 0.025, 0.010) and low computational complexity with CO-OFDM-MIMO-ELM, [35].…”

Section: Discussionmentioning

confidence: 70%

Highly Accurate Technique for CO-OFDM Channel Estimation Technique Using Extreme Learning Machine (ELM)

Joseph

Puttamadappa

2023

Wseas Transactions on Electronics

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In wireless systems, channel estimation is considered a problematic technology, due to the fact of the difference in time between wireless channels and the noise effect. Orthogonal frequency-division multiplexing (OFDM) is a promising candidate for future optical communications and has received wide concern. The article proposed a Coherent Optical (CO) orthogonal frequency division multiplexing (OFDM) scheme, which gives a scalable and flexible solution for increasing the transmission rate, being extremely robust to chromatic dispersion as well as polarization mode dispersion. Nevertheless, both coherent detection and OFDM are prone to phase noise due to the phase mismatch between the laser oscillators at the transmitter and receiver sides and the relatively long OFDM symbol duration compared to that of single carrier communications. An Extreme Learning Machine (ELM) with Pilot Assisted Equalization (PEM) is proposed for compensation of impairments caused by fibre nonlinearity in coherent optical communication systems. Channel estimation using ELM and the value of distortion is sent to the OSTBC receiving end based on the distortion information the data is decoded and pilot data is removed. FFT is applied to the data and QPSK demodulation is done in the data to get its original form. In addition, the article utilized a free-space optical communication system of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) with a modified receiver structure. Simulation reveals that the proposed model exhibits significant BER (0.0112) performance and provides better spectral efficiency as compared with conventional systems and less computational complexity. This suggested that the proposed method shows better performance by using the CO-OFDM-FSO-MIMO-ELM-based channel estimation technique for high-speed data communication networks in real-time scenarios respectively.

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Section: Discussionmentioning

confidence: 70%

Highly Accurate Technique for CO-OFDM Channel Estimation Technique Using Extreme Learning Machine (ELM)

Joseph

Puttamadappa

2023

Wseas Transactions on Electronics

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“…By contrast, the prediction method based on non‐model‐based technique lies on the channel's prior knowledge. Therefore, it is highly suitable for real‐time communication system 12 …”

Section: Related Workmentioning

confidence: 99%

Challenges and vision of wireless optical and acoustic communication in underwater environment

Menaka

Gauni

Manimegalai

et al. 2022

Int J Communication

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Summary Underwater Wireless Communication is a rapidly growing technology in the research domain. The researchers have been working towards the developments in underwater communication by investigating climatic changes, prediction of natural disasters, marine environment, monitoring aquatic life, oceanographic transmission, and data collection. The main objective of underwater communication is to achieve lossless, high transmission with the least power consumption. The Unmanned Underwater Vehicles (UUVs) and Autonomous Underwater Vehicles (AUVs) are set up with remote instruments and sensors to enable the natural exploration of the resources in the undersea environment. This research article is based on the study of various modes of communication, architectures, and protocol layers involved in underwater communication. Furthermore, an underwater hybrid connection is established by opto‐acoustic signals, and the overview of various performance characteristics, channel behaviors, and issues in the underwater scenario are investigated.

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“…Machado et al demonstrated the application of a neural network classifier for the detection of underwater acoustic signals, based on Hamming code transmission [10]. Liu et al proposed an integrated learning model consisting of a one-dimensional CNN and a long short-term memory (LSTM) network for orthogonal frequency division multiple access systems in underwater acoustic channels [11]. Furthermore, Miao et al presented a novel sparse anisotropic chirplet transform to reveal fine time-frequency structures of underwater acoustic signals, showing the superiority of CNN in underwater acoustic communication [12].…”

Section: Introductionmentioning

confidence: 99%

Variational Mode Decomposition for Deep Convolutional Neural Networks in Underwater Acoustic Communication

Jiang

Chen

Xing

2023

Preprint

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Recently, the application of deep convolutional neural networks (DCNN) in underwater acoustic communication has become a growing research hotspot. However, it is hindered by the large amounts of training data required to achieve a complex communication system. In this paper, we propose a novel approach of combining variational mode decomposition (VMD) and DCNN networks to address this issue. Considering filter-bank multicarrier (FBMC) technologies offer promising performance in terms of spectral efficiency and robustness to interference, and the Bellhop model is also capable of providing effective and accurate communication data, this study adopts FBMC technologies for signal modulation and Bellhop models for communication data. The results show that the VMD-DCNN equalizer has a lower BER compared with the other seven contrast methods, thus further promoting the application and development of VMD-DCNN technologies in underwater acoustic communications.

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Channel State Information Prediction for Adaptive Underwater Acoustic Downlink OFDMA System: Deep Neural Networks Based Approach

Cited by 48 publications

References 46 publications

Highly Accurate Technique for CO-OFDM Channel Estimation Technique Using Extreme Learning Machine (ELM)

Highly Accurate Technique for CO-OFDM Channel Estimation Technique Using Extreme Learning Machine (ELM)

Challenges and vision of wireless optical and acoustic communication in underwater environment

Variational Mode Decomposition for Deep Convolutional Neural Networks in Underwater Acoustic Communication

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