Underwater Acoustic Communication Channel Modeling using Deep Learning

Onasami, Oluwaseyi; Adesina, Damilola; Qian, Lijun

doi:10.1145/3491315.3491323

Cited by 13 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…UWCs researchers have been interested in artificial intelligence due to its better performance. Recently, UWA modern technologies has been introduced in a brief although there are many papers published dealing with UWAC [180][181][182]. UWWC's contemporary technologies are depicted in Figure 19.…”

Section: Uwa Modern Technologiesmentioning

confidence: 99%

A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems

Junejo

Sattar

Adnan

et al. 2023

JMSE

View full text Add to dashboard Cite

In the past decades, researchers/scientists have paid attention to the physical layer of underwater communications (UWCs) due to a variety of scientific, military, and civil tasks completed beneath water. This includes numerous activities critical for communication, such as survey and monitoring of oceans, rescue, and response to disasters under the sea. Till the end of the last decade, many review articles addressing the history and survey of UWC have been published which were mostly focused on underwater sensor networks (UWSN), routing protocols, and underwater optical communication (UWOC). This paper provides an overview of underwater acoustic (UWA) physical layer techniques including cyclic prefix orthogonal frequency division multiplexing (CP-OFDM), zero padding orthogonal frequency division multiplexing (ZP-OFDM), time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM), filter bank multicarrier (FBMC) modulation, MIMO, spatial modulation technologies (SMTs), and orthogonal frequency division multiplexing index modulation (OFDM-IM). Additionally, this paper provides a comprehensive review of UWA channel modeling problems and challenges, such as transmission loss, propagation delay, signal-to-noise ratio (SNR) and distance, multipath effect, ambient noise effect, delay spread, Doppler effect modeling, Doppler shift estimation. Further, modern technologies of the physical layer of UWC have been discussed. This study also discusses the different modulation technology in terms of spectral efficiency, computational complexity, date rate, bit error rate (BER), and energy efficiency along with their merits and demerits.

show abstract

Section: Uwa Modern Technologiesmentioning

confidence: 99%

A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems

Junejo

Sattar

Adnan

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…The deep learning (DL)‐based UAC channel model was presented in Onasami et al 25 This method uses real‐time data for the processing, which was gathered from Lake Tahoe. Both machine learning and deep learning (ie, deep neural network [DNN] and long short‐term memory [LSTM]) approaches were used for accurately estimating the UWA channel.…”

Section: Related Workmentioning

confidence: 99%

An improved underwater wireless sensor network communication using Internet of Things and signal to noise ratio analysis

Kapileswar

Polasi

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Underwater acoustic (UWA) channels are widely regarded as one of the most challenging communication mediums. Low frequencies are best for acoustic propagation, and the bandwidth available for communication is extremely small. The worst channel situation often limits the efficiency of UWA communication systems due to the difficulty and time‐varying nature of the UWA channel. To solve the existing limitations related with underwater signal transmission and communication, an improved underwater communication system incorporated with Internet of Things (IoT) is proposed in this work. The proposed system utilizes the orthogonal signal division multiplexing modulation technique. Here the signal transmission process is achieved with the help of an IoT network system. An improved AdaBoost channel estimation algorithm is used to obtain the channel information. This work utilizes an improved stochastic gradient descent optimization method for selecting the suitable mode of signal transmission based on the estimated channel. Further, an adaptive recursive least square channel equalization algorithm is used for channel compensation. The simulation results are obtained with the help of the MATLAB platform. The performance is analyzed in terms of parameters such as signal to noise ratio, bit error rate, and mean square error. A comparison of the proposed method is also made with the existing methods. The evaluation results show that the proposed method performs better than the existing methods.

show abstract

“…However, there has not been much research work done in the domain of UWA communications using machine learning because of the complex nature of the underwater environment and the lack of sufficient and high-quality data. This motivated us to leverage the capability of collecting real-world data of UWA communications from Lake Tahoe in Reno, Nevada, and apply a data-driven approach to modeling the UWA channel using machine learning on the collected datasets [11].…”

Section: Figurementioning

confidence: 99%

Underwater Acoustic Communication Channel Modeling Using Reservoir Computing

Onasami

Feng

et al. 2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment. In order to improve UWA communications, modeling and understanding the UWA channel is indispensable. However, there exist many challenges due to the high uncertainties of the underwater environment and the lack of real-world measurement data. In this work, the capability of reservoir computing and deep learning has been explored for modeling the UWA communication channel accurately using real underwater data collected from a water tank with disturbance and from Lake Tahoe. We leverage the capability of reservoir computing for modeling dynamical systems and provided a data-driven approach to modeling the UWA channel using Echo State Network (ESN). In addition, the potential application of transfer learning to reservoir computing has been examined. Experimental results show that ESN is able to model chaotic UWA channels with better performance compared to popular deep learning models in terms of mean absolute percentage error (MAPE), specifically, ESN has outperformed deep neural network by 2% and as much as 40% in benign and chaotic UWA respectively.

show abstract

Underwater Acoustic Communication Channel Modeling using Deep Learning

Cited by 13 publications

References 15 publications

A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems

A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems

An improved underwater wireless sensor network communication using Internet of Things and signal to noise ratio analysis

Underwater Acoustic Communication Channel Modeling Using Reservoir Computing

Contact Info

Product

Resources

About