Adaptive Modulation for Long-Range Underwater Acoustic Communication

Long-range underwater acoustic communication (LR-UWAC) refers to the peer-to-peer transmission of messages for distances of tens to hundreds of km. It is a key enabling technique for applications such as control over unmanned underwater vehicles for long-term surveying. While underwater acoustic com...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2020-10, Vol.19 (10), p.6844-6857
Main Authors: Huang, Jianchun, Diamant, Roee
Format: Article
Language:English
Subjects:
Acoustic surveying
Acoustics
adaptive modulation
Autonomous underwater vehicles
channel classification
channel simulation
Classification
Communication
long-range underwater acoustic communications
Modulation
OFDM
Phase shift keying
Receivers
Surveying
Throughput
Transmitters
Underwater acoustics
Underwater communication
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Summary:Long-range underwater acoustic communication (LR-UWAC) refers to the peer-to-peer transmission of messages for distances of tens to hundreds of km. It is a key enabling technique for applications such as control over unmanned underwater vehicles for long-term surveying. While underwater acoustic communication over shorter ranges is an established technique, this is not the case for LR-UWAC. This gap is mostly due to channel uncertainties: in the absence of feedback from the receiver and due to the long transmission range, channel state information (CSI) at the transmitter may not reflect the actual channel. In this paper, we propose an adaptive approach to pre-set the modulation scheme for LR-UWAC. This is a channel classification approach which, based on environmental information and on prior training on various channel types, predicts the best modulation scheme for the expected channel. Our classification procedure is trained to identify the channel's important features. Thus, compared to a direct decision approach, it becomes less sensitive to possible mismatches of environmental information. Our numerical simulation and sea experiment show that our approach successfully identifies the best modulation scheme based on the environmental information - even when the information is biased or only partially available.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2020.3006230