TY - GEN
T1 - Application of Machine Learning to Signal Detection in Underwater Wireless Optical Communication Links
AU - Nennouche, Mohamed
AU - Khalighi, Mohammad Ali
AU - Dowhuszko, Alexis
AU - Merad, Djamal
AU - Boi, Jean Marc
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - We consider the application of a machine-learning (ML)-based method to the demodulation of the received signal in underwater wireless optical communication (UWOC) links. This approach is justified when the underwater optical channel is subject to strong variations due to various phenomena such as pointing errors and turbulences, which directly impact the received optical power, requiring accurate and agile channel estimation. The investigated ML method is based on the well-known K-nearest neighbors (KNN). We demonstrate excellent link performance for different types of modulation schemes even under high data rates and low received optical powers, for instance, achieving effective bit rates of 2.96 and 2.54 Gbps using 16-QAM and 32-QAM modulation schemes, respectively, at a received optical power of -16.4 dBm. We also discuss the implementation aspects of the proposed approach, including its computational complexity.
AB - We consider the application of a machine-learning (ML)-based method to the demodulation of the received signal in underwater wireless optical communication (UWOC) links. This approach is justified when the underwater optical channel is subject to strong variations due to various phenomena such as pointing errors and turbulences, which directly impact the received optical power, requiring accurate and agile channel estimation. The investigated ML method is based on the well-known K-nearest neighbors (KNN). We demonstrate excellent link performance for different types of modulation schemes even under high data rates and low received optical powers, for instance, achieving effective bit rates of 2.96 and 2.54 Gbps using 16-QAM and 32-QAM modulation schemes, respectively, at a received optical power of -16.4 dBm. We also discuss the implementation aspects of the proposed approach, including its computational complexity.
KW - KNN classification
KW - Machine learning
KW - Signal demodulation
KW - Underwater wireless optical communications
UR - http://www.scopus.com/inward/record.url?scp=85203677815&partnerID=8YFLogxK
U2 - 10.1109/CSNDSP60683.2024.10636416
DO - 10.1109/CSNDSP60683.2024.10636416
M3 - Conference article in proceedings
AN - SCOPUS:85203677815
T3 - 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2024
SP - 534
EP - 538
BT - 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2024
PB - IEEE
T2 - International Symposium on Communication Systems, Networks and Digital Signal Processing
Y2 - 17 July 2024 through 19 July 2024
ER -