Hybrid Deep Learning-Based Adaptive Multiple Access Schemes Underwater Wireless Networks

Anitha, D.; Karthika, R.

doi:10.32604/iasc.2023.023361

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…The full experiment is conducted and different hybrid learning models and conventional techniques are examined. The results of simulations show that the suggested hybrid learning models outperformed the other models in implementing the communications schemes under dynamic underwater conditions, reaching approximately 98% accuracy and a 30% increase in BER performance [16].…”

Section: Review Of Literaturementioning

confidence: 94%

Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

Sudhanshu Tyagi

2023

tjjpt

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This study's goal is to provide an early analysis of deep learning (DL) for signal identification in wireless systems that use non-orthogonal multiple access (NOMA). The successive interference cancellation (SIC) approach is frequently used at the receiver in NOMA systems when several users are decoded successively. Without explicitly calculating channels, a DL-based NOMA receiver can decode messages for several users at once. To estimate the multiuser uplink channel (CE) and recognize the initial broadcast signal in this study, it is recommended that a deep neural network with bi-directional long short-term memory (Bi-LSTM) be utilized. The suggested Bi-LSTM model, in contrast to conventional CE techniques, may immediately retrieve transmission signals impacted by channel distortion. During the offline training phase, the Bi-LTSM model is trained using simulation data based on channel statistics. The trained model is then applied to retrieve the transmitted symbols in the stage of online deployment. According to the findings, the DL method could outperform a maximum probability detector that considers interference effects when inter-symbol interference is substantial.

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Section: Review Of Literaturementioning

confidence: 94%

Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

Sudhanshu Tyagi

2023

tjjpt

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“…In 2023, Anitha et al, [ 31 ] proposed a novel intelligent selection method for modulation schemes in UWA communication systems. The modulation schemes considered in the study are Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), and Orthogonal Frequency Division Multiplexing (OFDM).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Alqahtani GJ et al [ 30 ] introduced the Mobility prediction optimal data forwarding (MPODF) protocol for UASNs, incorporating a realistic and physically inspired mobility model, achieving high packet delivery ratio, energy efficiency, and reduced end-to-end delay. Anitha et al [ 31 ] proposed an intelligent selection method for modulation schemes in UWA communication systems using a hybrid learning model, achieving a high accuracy rate and improved Bit Error Rate (BER) performance. Sathish et al [ 32 ] proposed energy-balanced reliable and effective clustering for underwater wireless sensor networks.…”

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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Hybrid Deep Learning-Based Adaptive Multiple Access Schemes Underwater Wireless Networks

Cited by 2 publications

References 27 publications

Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

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

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