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.

A space–air–ground–sea integrated network (SAGSIN) is a promising heterogeneous network framework for the next generation mobile communications. Moreover, federated learning (FL), as a widely used distributed intelligence approach, can improve advanced network performance. In view of the combination and cooperation of SAGSINs and FL, an FL-based SAGSIN framework faces a number of unprecedented challenges, not only from the communication aspect but also on the security and privacy side. Motivated by these observations, in this article, we first give a detailed state-of-the-art review of recent progress and ongoing research works on FL-based SAGSINs. Then, the challenges of FL-based SAGSINs are discussed. After that, for different service demands, basic applications are introduced with their benefits and functions. In addition, two case studies are proposed, in order to improve SAGSINs’ communication efficiency under a significant communication latency difference and to protect user-level privacy for SAGSIN participants, respectively. Simulation results show the effectiveness of the proposed algorithms. Moreover, future trends of FL-based SAGSINs are discussed.

This paper proposes a joint multi-task Bayesian channel estimation and data detection algorithm for Turbo equalization (TEQ) in underwater acoustic (UWA) communication. The joint channel estimation and data detection (JCED) problem is formulated as a multi-task sparse Bayesian learning framework in single carrier (SC) communications. The framework treats the equalized symbols as unknown variables for improving the performance of iterative equalization and leverages temporal correlation in UWA channels by partitioning received symbols into subblocks. Furthermore, a JCED algorithm is derived with variational Bayesian inference. The proposed algorithm was evaluated based on the underwater field data collected during a lake experiment conducted in Qiandao Lake, Zhejiang province, China, in May 2016. The performance of the proposed algorithm has been validated with simulation and experiment results.

It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE.

Dynamic underwater acoustic network is a research hotspot. Due to the complexity and time-varying of underwater acoustic channel, the performance of single-mode underwater acoustic communication system is often limited by the worst channel situation. Orthogonal signal division multiplexing (OSDM) is a new signal modulation technology, which can be regarded as a bridge between single carrier modulation and orthogonal frequency division multiplexing (OFDM) modulation. In order to improve the channel adaptive ability of underwater acoustic communication system and obtain a unified architecture of different carrier modulation, an adaptive multi-mode underwater acoustic communication system based on OSDM and direct sequence spread spectrum modulation (AMMUAC/OS) is proposed in this paper. Combined with spread spectrum modulation, the system can realize single carrier time domain spread spectrum (SC-TDSS), single carrier system with cyclic prefix (SC-CP), OSDM, multicarrier frequency domain spread spectrum (MC-FDSS), and OFDM communication system by adjusting the vector length M of OSDM. The addition of a variety of digital mapping methods enables the system to achieve multi-mode communication with a unified architecture. Based on the relationship between the OSDM vector length M and the underwater acoustic channel delay, a joint adaptive decision criterion based on the maximum channel delay and the channel signal-to-noise ratio (SNR) is proposed. Firstly, the OSDM vector length M is determined according to the maximum channel delay, and then the system communication mode is selected according to the channel SNR. Orthogonal matching pursuit (OMP) channel estimation algorithm is used to obtain channel information and minimum mean square error (MMSE) channel equalization algorithm is used for channel compensation. An adaptive multi-mode data frame structure is designed for the interaction between the sender and the receiver. The simulation results show that the proposed AMMUAC/OS can adapt to underwater acoustic channels with different channel delay and SNR.