| 한글제목(Korean Title) |
Long Short-Term Memory Neural Network assisted Peak to Average Power Ratio Reduction for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communication |
| 영문제목(English Title) |
Long Short-Term Memory Neural Network assisted Peak to Average Power Ratio Reduction for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communication |
| 저자(Author) |
Waleed Raza
Xuefei Ma
Houbing Song
Amir Ali
Habib Zubairi
Kamal Acharya
|
| 원문수록처(Citation) |
VOL 17 NO. 01 PP. 0239 ~ 0260 (2023. 01) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
The underwater acoustic wireless communication networks are generally formed by the different autonomous underwater acoustic vehicles, and transceivers interconnected to the bottom of the ocean with battery deployed modems. Orthogonal frequency division multiplexing (OFDM) has become the most popular modulation technique in underwater acoustic communication due to its high data transmission and robustness over other symmetrical modulation techniques. To maintain the operability of underwater acoustic communication networks, the power consumption of battery-operated transceivers becomes a vital necessity to be minimized. The OFDM technology has a major lack of peak to average power ratio (PAPR) which results in the consumption of more power, creating non-linear distortion and increasing the bit error rate (BER). To overcome this situation, we have contributed our symmetry research into three dimensions. Firstly, we propose a machine learning-based underwater acoustic communication system through long short-term memory neural network (LSTM-NN). Secondly, the proposed LSTM-NN reduces the PAPR and makes the system reliable and efficient, which turns into a better performance of BER. Finally, the simulation and water tank experimental data results are executed which proves that the LSTM-NN is the best solution for mitigating the PAPR with non-linear distortion and complexity in the overall communication system. |
| 키워드(Keyword) |
underwater acoustic communication
orthogonal frequency divisional multiplexing
long short-time memory neural network
machine learning
PAPR
|
| 파일첨부 |
PDF 다운로드
|
