Adaptive Modulation and Coding for Underwater Acoustic OTFS Communications Based on Meta-Learning

This letter proposes an adaptive modulation and coding (AMC) scheme based on deep learning for underwater acoustic (UWA) communications. To achieve good communication performance in fast time-varying UWA channels, the proposed AMC scheme is implemented on the orthogonal time-frequency space (OTFS) m...

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Vydáno v:IEEE communications letters Ročník 28; číslo 8; s. 1845 - 1849
Hlavní autoři: Jing, Lianyou, Dong, Chaofan, He, Chengbing, Shi, Wentao, Wang, Han, Zhou, Yi
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
adaptive modulation and coding
Algorithms
Artificial neural networks
Channels
Coding
convolutional neural network
Convolutional neural networks
Deep learning
Feature extraction
Kernel
Machine learning
meta-learning
Modulation
orthogonal time frequency space
Symbols
Task analysis
Time-frequency analysis
Underwater acoustic communications
Underwater acoustics
ISSN:1089-7798, 1558-2558
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Shrnutí:This letter proposes an adaptive modulation and coding (AMC) scheme based on deep learning for underwater acoustic (UWA) communications. To achieve good communication performance in fast time-varying UWA channels, the proposed AMC scheme is implemented on the orthogonal time-frequency space (OTFS) modulation system. We design an end-to-end deep convolutional neural network (CNN) to capture the channel features and determine the optimal modulation and coding scheme. Additionally, we utilize a meta-learning algorithm to address environment mismatch in real-world UWA applications. This algorithm effectively adapts the CNN model from a given UWA environment to a new UWA environment with only a small amount of data. The performance of the proposed scheme is verified through real-world measured channels. Simulation results demonstrate that the proposed method outperforms existing machine learning-based AMC and fixed modulation and coding schemes in various UWA scenarios, offering better communication throughput and stronger learning capabilities.
Bibliografie:ObjectType-Article-1
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2024.3418192