Performance Analysis of Deep Learning based Signal Constellation Identification Algorithms for Underwater Acoustic Communications
| Autor: | A.E. Abdelkareem |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: |
Acoustic communications
Complexity analysis Deep learning Constellation learning Recurrent Neural Networks (RNNs) Engineering machinery tools and implements TA213-215 Mechanics of engineering. Applied mechanics TA349-359 Electrical engineering. Electronics. Nuclear engineering TK1-9971 Chemical engineering TP155-156 Environmental engineering TA170-171 |
| Zdroj: | Diyala Journal of Engineering Sciences, Vol 17, Iss 3 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1999-8716 2616-6909 |
| DOI: | 10.24237/djes.2024.17301 |
| Popis: | This research delves into the evaluation of Deep learning signal constellation identification (DL-SCI) algorithms in underwater acoustic communications using Orthogonal Frequency Division Multiplexing (OFDM). It distinctly examines at how effective the recurrent neural networks (RNNs), particularly, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) algorithms in predicting the signal constellation when applied to different underwater acoustic channels characteristics. Unlike manual feature selection in machine learning (ML), in this paper, DL-SCI exploits the labelled OFDM signals at the transmitter to detect and decode them at the receiver. In order to measure their effectiveness performance metrics, Bit Error Rate (BER) and parameters derived from the confusion matrix such as accuracy and precision are used. The study highlights the importance of utilizing zero cyclic prefix techniques which can exploit the inherent bandwidth limitation effectively. Furthermore, when examining complexity, it is observed that both GRU and LSTM algorithms require less floating-point operations (FLOPS) compared to traditional methods such as Minimum Mean Square Error (MMSE) and Least Squares (LS). Interestingly GRU shows performance in terms of complexity when compared to LSTM. Moreover, GRU outperforms LSTM by achieving a 4 dB improvement for long subcarriers. These results emphasize the effectiveness of learning techniques in enhancing performance and efficiency in acoustic communications. |
| Databáze: | Directory of Open Access Journals |
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