The development of integrated satellite-terrestrial networks has gained significant attention from both industry and academia in recent years, owing to their potential for delivering low latency, high dependability, strong resilience, ubiquitous connectivity and global broadband coverage services. However, due to the ever-changing nature of satellite topology and the complexity of diverse integrated satellite-terrestrial networks, routing requests is challenging. In this paper, the vehicle movement is uncertain introducing the intermittent connectivity related to vehicles. Therefore, we propose a distributionally robust optimization (DRO) model to minimize, under uncertain latency probability distributions, the expected worst-case overall task routing delay from source to target user equipment through satellite constellation. The model addresses undetermined uploading and downloading latency between automobiles, satellites, and user equipment by employing the Wasserstein ambiguity set, allowing for unpredictable vehicle mobility and intermittent connections. By reformulating the problem into a tractable form, we determine the optimal routing path for task uploading, satellite constellation, and task downloading. Ultimately, the performance of the proposed DRO model demonstrates the model’s ability to address the challenges of integrated satellite-terrestrial network routing.

Due to the ability to provide low latency, high dependability, and worldwide broadband coverage services, the development of integrated satellite-terrestrial networks has attracted significant interest from both industry and academia over the past few decades. However, the dynamic satellite topology, heterogeneous, expansive, and intricate properties of the integrated satellite-terrestrial network make routing tasks difficult. In this research, we design the distributionally robust optimization (DRO) model with the objective of minimizing the estimated worst-case total task routing delay from the source mobile devices to the matching target mobile devices under an uncertain probability distribution. Taking into account the unpredictable vehicle movement and discontinuous connection between vehicles and mobile devices, the indeterminate offloading and downloading from automobiles to satellites and mobile devices, respectively, are captured by the Wasserstein ambiguity set. Then, we are able to determine the optimal route for task uploading, routing throughout the satellite constellation, and downloading. Finally, experimental results demonstrate that our proposed model has a lower and more robust latency than that of robust optimization (RO) strategy.

Satellite communication is one primary structure of the future wireless systems. Many corporations and academic institutions are committed to this research, e.g., StarLink. Their aim is to construct a constellation of satellite networks covering the whole world. Thanks to this design, our mobile devices can connect to the Internet anywhere in the world without being restricted by the coverage area of base stations. However, given that the constellation of satellites around the Earth is a system that varies in time, algorithms must adapt to the dynamic topology. To solve the problem, in this paper we investigate how to send data from the base station to the target satellite with minimal delay. We systematically formulate the transmission limitations, uplink, and downlink transmission delay. With well defined limits and delays on space-terrestrial transmissions, we can set rewards and punishments by mathematical analysis to simulate the agent in deep reinforcement learning (DRL), which can explore all choices in the system and optimize the routing algorithm. Finally, the simulation results corroborate a fully functional space-terrestrial network constellation to simulate the actual situation.

With the explosive growth of low earth orbit (LEO) satellite networks, such as Starlink, satellite communication has lower latency and can achieve high-speed transmission than before. However, the time-variant topology during all network lifetimes makes the routing problem in the LEO satellite networks challenging. Therefore, in this paper, we propose the deep reinforcement learning-based satellite routing (DRL-SR) method to tackle the multi-commodity flow routing problem in the LEO satellite networks. Given the current state of the satellite network environment, the satellite operation center will determine how to route the requests to the matching destinations. Particularly, the single agent in our DRL-SR approach can determine the multiple next hops as actions for all the corresponding requests each timeslot. Finally, simulation results show that our proposed algorithm yields lower latency than the shortest path approach.