TY - GEN
T1 - Acoustic Channel-aware Autoencoder-based Compression for Underwater Image Transmission
AU - Anjum, Khizar
AU - Li, Zhile
AU - Pompili, Dario
N1 - Funding Information:
Acknowledgements: We thank Rutgers UG student Christopher Yeh for helping out with the study. This work was supported via the NSF NeTS Award No. CNS-1763964.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Image transmission in Underwater Internet of Things (UW IoT) is a challenging problem due to the characteristic low bandwidth and variable path loss of the underwater acoustic channel. However, to enable intelligent and collaborative exploration of the underwater environment, such a communication is of paramount importance. To address such challenges, a reliable and energy-efficient Machine Learning (ML)-based underwater image transmission system is proposed where images are compressed using a data-based approach and robust compression codes are learned. The system uses an Autoencoder (AE) to enable intelligent, data-driven selection of coding parameters. The AE is evaluated in the presence of underwater acoustic fading channel information to achieve efficient and robust image transmission, and is compared against model-based approaches.
AB - Image transmission in Underwater Internet of Things (UW IoT) is a challenging problem due to the characteristic low bandwidth and variable path loss of the underwater acoustic channel. However, to enable intelligent and collaborative exploration of the underwater environment, such a communication is of paramount importance. To address such challenges, a reliable and energy-efficient Machine Learning (ML)-based underwater image transmission system is proposed where images are compressed using a data-based approach and robust compression codes are learned. The system uses an Autoencoder (AE) to enable intelligent, data-driven selection of coding parameters. The AE is evaluated in the presence of underwater acoustic fading channel information to achieve efficient and robust image transmission, and is compared against model-based approaches.
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U2 - 10.1109/UComms56954.2022.9905691
DO - 10.1109/UComms56954.2022.9905691
M3 - Conference contribution
AN - SCOPUS:85141578656
T3 - 2022 6th Underwater Communications and Networking Conference, UComms 2022
BT - 2022 6th Underwater Communications and Networking Conference, UComms 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th Underwater Communications and Networking Conference, UComms 2022
Y2 - 30 August 2022 through 1 September 2022
ER -