Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system

Cui, Xuerong; Yan, Peihao; Li, Juan; Li, Shibao; Liu, Jianhang

doi:10.1186/s13634-022-00961-5

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…This kind of transmission is useful for environmental monitoring, rapid response to natural disasters, pollution prevention, application of protection applications, the safety of off-shore ports and sectors and conducting various types of ocean and topographical research. Because of its numerous benefits, UASNs perform a vital and essential part in maritime innovation [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

Kaveripakam,

Chinthaginjala

2023

PLoS ONE

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The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel’s time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70.

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Section: Introductionmentioning

confidence: 99%

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

Kaveripakam,

Chinthaginjala

2023

PLoS ONE

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“…To develop and enhance e cient underwater acoustic communication systems, it is crucial to conduct research and have a thorough understanding of how the underwater environment affects communication signals (Cui et al 2023). One method for replicating the effects of the underwater acoustic channel on the channel is to replicate the characteristics of the actual aquatic environment (Huang et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Classification and Modelling of Underwater Acoustic Communication through Machine Learning and Image Processing Technique

Shaik,

Das

2023

Preprint

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The increasing prevalence of underwater activities has highlighted the urgent need for reliable underwater acoustic communication systems. However, the challenging nature of the underwater environment poses significant obstacles to the implementation of conventional voice communication methods. To better understand and improve upon these systems, simulations of the underwater audio channel have been developed using mathematical models and assumptions. In this study, we utilize real-world informationgathered from both a measured water reservoir and Lake to evaluate the ability of machine learning and machine learning methods, specifically Long Short-Term Memory (LSTM) and Deep Neural Network (DNN), to accurately reconstruct the underwater audio channel. The outcomesvalidate the efficiency of machine learning methods, particularly LSTM, in accurately simulating the underwater acoustic communication channel with low mean absolute percentage error. Additionally, this research also includes an image processing to identify the objects present thein theacoustic environment.

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“…Adaptive Modulation and Coding: Adjusts modulation and coding schemes according to channel conditions, balancing data rate and error performance for reliable communication in varying underwater environments [6].…”

mentioning

confidence: 99%

Enhancing Energy Efficiency of Underwater Sensor Network Routing Aiming to Achieve Reliability

Tarif,

Nouri Moghadam

2023

Preprint

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To facilitate wireless communication, ocean exploration, and various environmental and research applications, the underwater Internet of Things has been developed with an infrastructure of acoustic sensor networks. There has been a significant increase in failures and a decrease in the efficiency of the network due to the fluid and challenging underwater environment. Consequently, achieving quality of service (QoS) in underwater networks remains a research area. Supporting QoS in underwater sensor networks is challenging due to resource limitations such as processing power, memory, bandwidth, and power sources. The purpose of this study was to examine the service quality requirements as well as protocols that have been implemented in recent years. Finally, based on quality criteria, we have presented a routing solution based on fuzzy logic, which is more efficient than other recent methods regarding energy consumption rate, JFI, efficiency, and reliability.

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Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system

Cited by 11 publications

References 27 publications

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

A Novel Method for Classification and Modelling of Underwater Acoustic Communication through Machine Learning and Image Processing Technique

Enhancing Energy Efficiency of Underwater Sensor Network Routing Aiming to Achieve Reliability

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