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.

Flexible photoelectric film is an anisotropic material. The slight change of equipment performance during processing is prone to cause deformation of the material. Therefore, it is important to predict the degradation of processing equipment performance. Since the performance degradation of flexible photoelectric film material Roll-to-Roll (R2R) processing equipment is a nonlinear process, this paper introduces an adaptive fuzzy clustering method to construct a fuzzy membership function model for calculating the performance degradation index of R2R processing equipment and studies the parameter solving method such as the AFCM division of the roller vibration data, the category center value of the fuzzy membership function, and the input data division area width. Finally, the performance degradation index calculation algorithm is designed. The roller shaft accelerated life test was carried out using self-made equipment. The test data were 1000 sets. The results showed that the root mean square eigenvalues and the kurtosis eigenvalues of the roller vibration data are sensitive to the performance degradation. The equipment performance curve described by the first and second types of performance degradation indicators was very stable in the early stage. After the 800th group, the curve continued to decrease, and the change was more severe, indicating that the performance degradation of the equipment is more serious. In the 980th group, the longer-lasting roller shaft was damaged, and the performance index value was about zero, which proved the correctness of the performance degradation prediction method proposed in this paper in calculating the performance degradation value of the equipment.

We consider a digital signal processing sensor array system based on randomly distributed sensor nodes for intrusion and surveillance applications. The nodes having acoustical, seismic, and other sensors are self organized into a synchronized network using low powered spread spectrum transceivers. Beamforming array techniques for enhanced detection and estimation and performances under various ideal and practical conditions are presented.

Many marine applications are required to obtain data from underwater sources and transmit it to a data center situated on the water or land. The data transmissions must cross the water-air interface using a surface gateway. In this process, the gateway traditionally demodulates underwater acoustic (UWA) signals and forwards them via a radio link. This paper proposes a novel cloud software-defined acoustic (C-SDA) framework for cross-interface relaying. In contrast to the conventional approach of demodulating UWA signals at the gateway, the C-SDA framework employs a sampling and forward (SF) relaying strategy. This method only samples the passband UWA signals, effectively circumventing the necessity for dual-stack packet encapsulation and re-encapsulation at the gateway. Since passband sampling may result in a large amount of data to the radio link, SF relaying uses radio communication bandwidth to exchange cloud computation resources. C-SDA also implements softwarization of the UWA transceiver functions in the cloud (i.e., the monitoring center). Thus, the challenging UWA channel can be addressed by the advanced but complex algorithms in the cloud. C-SDA not only simplifies the design of the gateway but also facilitates rapid technique updates and iterations for the UWA transceiver. The field-test experimental results show that the proposed C-SDA can enable complex equalizers and achieve significant bit error rate improvement compared to the self-developed embedded UWA receiver. Additionally, the future research directions for C-SDA are discussed in the paper.

Roll-to-Roll (R2R) processing is a common processing method for flexible photoelectric film materials. Due to the physical properties of the materials, the change in the performance of the R2R processing equipment can easily cause deformation of the flexible film material, it is particularly important to predict the performance degradation of the processing equipment. Based on the accuracy and real-time requirements of performance degradation prediction, a PTS-FNN model for performance degradation prediction was proposed in this paper, which combines the Possibilistic C-Means (PCM) fuzzy clustering and Takagi–Sugeno Fuzzy Neural Network (TS-FNN). We also studied the PCM classification algorithm of input data of PTS-FNN model, the predecessor network of TS-FNN prediction model and the construction method of post-component network. Finally, the implementation process of PCM classification algorithm and TS-FNN prediction model were given. The R2R processing equipment health prediction experiment system was built and the PTS-FNN model experiment was carried out. The experimental results showed that the training time of PTS-FNN model was 50.37% less than the standard TS-FNN prediction model. The prediction accuracy increased by 5.48%, and the PTS-FNN had no error in the judgment of state 1 and state 4.

Learning-based equalizers for multicarrier communication systems have been widely studied over underwater acoustic (UWA) channels. In this article, a learning-based equalizer is utilized for single-carrier (SC) underwater acoustic communications. A comprehensive comparison is made between existing deep learning (DL)-based approaches and a classical equalizer designed with adaptive filtering principles. It motivates the design of equalization for SC communications over underwater acoustic channels. To overcome distortion over the UWA channel, we propose a sliding deep learning-based equalizer that uses a sliding nonlinear network for equalization rather than a single-layer linear method. Moreover, to accelerate convergence during training, we proposed a preprocessing-based training phase. To mitigate the impact of time-varying channels, we additionally propose a meta-learning-enhanced adaptive filter algorithm for online adaptive equalization, named Meta-DNN. Based on the proposed DL equalizer, we leverage the pilot and data relationship to perform online transfer to achieve better BER performance. Moreover, to make this work more convincing, we test bit-error-rate (BER) performance across reproducible, realistic multi-scenario channels.