Recently, many satellites are being launched for providing global internet broadband service to individual consumers. Since satellites and users could move separately, providing seamless connectivity has become one of the most important tasks for mobile satellite networks. Current handover methods are based on either signal strength or service time, however, due to the randomness of user terminal (UT) arrivals and the unbalanced traffic distribution for high-mobility satellite networks, the success rate can hardly be guaranteed. To this end, we propose a QoE-driven intelligent handover mechanism for user-centric mobile satellite networks, through which the access satellites can be selected by predicted service time and communication channel resources. Accordingly, to ensure the service duration, a spatial relationship coupling model is proposed to predict relative motion pattern between UT and satellites; To improve the effectiveness of handover, an available channel estimation model is then developed based on the mobility pattern of adjacent satellites. Finally, reinforcement learning is adopted to maximize the UT's Quality of Experience (QoE) through predicted handover factors. Simulation results show that the proposed handover mechanism outperforms the conventional methods in terms of handover times, handover success rate, and end-to-end delay.

Three-dimensional sound-speed distribution is essential for large-scale underwater acoustic applications due to its influence on the signal propagation trajectory. However, it is labor, energy, and time consuming to measure sound speed by traditional methods because of the weak system maneuverability. In this article, an autonomous-underwater-vehicle-assisted underwater sound-speed inversion framework that collaborates ray tracing and artificial intelligence model is proposed to quickly obtain 3-D sound-speed distribution through multicoordinate inversions. An autoencoding-translation neural network is proposed to establish the nonlinear relationship from signal propagation time to the sound-speed profile (SSP), and the inversion time can be shortened with once forward propagation through the model. Robustness could be improved by inverting error-resistant implicit features into the SSP through the proposed translating neural network, whereas the implicit features are extracted from the autoencoder by denoising reconstruction of the input time information. To solve the overfitting problem and extend the training data set, virtual SSPs based on sparse feature points of real SSPs are generated. Simulation results show that our approach can provide a reliable and instantaneous monitoring of 3-D sound-speed distribution.

Real-time and accurate construction of regional sound speed profiles (SSPs) is important for building underwater positioning, navigation, and timing (PNT) systems as it greatly affects signal propagation modes. In this paper, we summarize and analyze the current research status in the field of underwater SSP construction, where the mainstream methods include direct SSP measurement and SSP inversion. For the direct measurement method, we compare the performance of popular international and commercial brands of temperature, conductivity, and depth profilers (CTDs). For the inversion methods, the framework and basic principles of matched field processing (MFP), compressive sensing (CS), and deep learning (DL) are introduced, and their advantages and disadvantages are compared. Presently, SSP inversion relies on sonar observation data, limiting its applicability to areas that can only be reached by underwater observation systems. Furthermore, these methods are unable to predict the distribution of sound velocity in future time. Therefore, the mainstream trend in future research on SSP construction will involve comprehensive utilization of multi-source data to offer elastic sound velocity distribution estimation services for underwater users without the need for sonar observation data.

We design an ocean surface drifting buoy system based on an unmanned aerial vehicle (UAV)-enabled wireless powered relay network in which the UAV acts as mobile hybrid access point that broadcasts energy to all buoys in the downlink and forwards information from the buoys to a ship signal tower (ST) in the uplink. In order to maximize the resource allocation efficiency of the system, due to the different initial energy reserve of the buoys, a novel communication mode selection strategy is proposed. In the direct transmission mode (DT mode), an energy-sufficient buoy transmits information directly to the ST, and in the relay transmission mode (RT mode), an energy-insufficient buoy relays information to the ST through the UAV. By applying the block coordinate descent and successive convex optimization, a joint UAV trajectory and resource allocation algorithm is proposed to maximize the minimum throughput of the buoys to work in the RT mode. Simulation results show that the proposed algorithm can significantly improve the minimum throughput of the ocean surface drifting buoys.