Research Area:  Machine Learning

Abstract:

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 proposed to train the DNN-based receiver by exploiting the model parameters from multiple buoys. We analyze the convergence performance of FML and derive a closed-form expression for the convergence rate, accounting for the impacts of scheduling ratios, local epochs, and data volumes on a single node. Simulation results show that the proposed C-DNN receiver that is trained with sufficient data achieves better bit error rate performance and lower complexity than classical matched filter (MF)-based detectors. The proposed FML also outperforms the MF-based method after several communication rounds and achieves better generalization performance than federated learning based systems.

Keywords:  
Receivers
Radio frequency
Chirp modulation
Convergence
Training
Chirp
Data models

Author(s) Name:  Hao Zhao; Fei Ji; Qiang Li; Quansheng Guan

Publisher name:  IEEE

DOI:  10.1109/JSTSP.2022.3144020

Volume Information:  Volume: 16

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9684939