Federated Meta-Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things

@article{Zhao2022FederatedME,
  title={Federated Meta-Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things},
  author={Hao Zhao and Fei Ji and Qiang Li and Quansheng Guan and Shuai Wang and Miaowen Wen},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  year={2022},
  volume={16},
  pages={474-486}
}
  • Hao Zhao, Fei Ji, Miaowen Wen
  • Published 24 May 2021
  • Computer Science
  • IEEE Journal of Selected Topics in Signal Processing
Ocean of Things, consisting of multiple buoys distributed on the sea, is an acoustic radio cooperative wireless network that aims to acquire underwater information. In this paper, a deep neural network (DNN)-based receiver with data augmentation, termed chirp (C)-DNN, is developed for a buoy that uses chirp modulation-based underwater acoustic communications. To further solve the problem that the training data at a single buoy may not be sufficient, a federated meta-learning (FML) scheme is… 

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