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Adaptive switching for communication profiles in underwater acoustic modems based on reinforcement learning

APPLIED ACOUSTICS (2023)

Deep Reinforcement Learning-Based Adaptive Modulation for Underwater Acoustic Communication with Outdated Channel State Information

Yuzhi Zhang et al.

Summary: This paper proposes a deep Q-network (DQN)-based adaptive modulation (AM) method for underwater acoustic communication, which combines the advantages of DQN and long short-term memory (LSTM) neural networks. Experimental results demonstrate that this method outperforms traditional methods in extracting channel information and selecting appropriate modulation modes.

REMOTE SENSING (2022)

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Article Computer Science, Information Systems

Reinforcement Learning-Based Adaptive Modulation and Coding for Efficient Underwater Communications

Wei Su et al.

IEEE ACCESS (2019)

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Proceedings Paper Computer Science, Interdisciplinary Applications

Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks

Mateus P. Mota et al.

2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS) (2019)

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Article Computer Science, Information Systems

Online Modeling and Prediction of the Large-Scale Temporal Variation in Underwater Acoustic Communication Channels

Wensheng Sun et al.

IEEE ACCESS (2018)

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Adaptive switching for communication profiles in underwater acoustic modems based on reinforcement learning

相关参考文献

Deep Reinforcement Learning-Based Adaptive Modulation for Underwater Acoustic Communication with Outdated Channel State Information

Reinforcement Learning-Based Adaptive Modulation and Coding for Efficient Underwater Communications

Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks

Online Modeling and Prediction of the Large-Scale Temporal Variation in Underwater Acoustic Communication Channels

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