Matching Game-Based Resource Allocation for Space-Surface-Submarine Networks

Low-earth orbit (LEO) satellite-assisted marine communication networks have become a research focus with the growth of marine activities. However, establishing communication links between underwater devices and maritime satellites is a challenge. Additionally, dynamic environments and multidomain media pose significant challenges in allocating resources effectively within this network. To address these issues, this paper constructs a Space-Surface-Submarine Unmanned Network (3SUN) incorporating LEO satellites, unmanned surface vehicles (USVs), and unmanned underwater vehicles (UUVs). We formulate the resource allocation problem in the 3SUN as a satellite revenue maximization problem. We propose a satelliteprioritized restricted three-sided matching algorithm to solve the match within a single time slot. Additionally, we incorporate deep reinforcement learning (DPL), using the previous stable matching results as training initialization to tackle dynamic connections across multiple slots. Simulation results show that our algorithm achieves satellite revenue closer to the optimal solution compared to other methods while maintaining lower time complexity.