LEO Satellite Connectivity for flying vehicles

Abstract: Compared with the terrestrial network (TN), which can only support limited covered areas, satellite communication (SC) can provide global coverage and high survivability in case of an emergency like an earthquake. Especially low-earth orbit (LEO) satellites, as a promising technology, which is integral to achieving the goal of global seamless coverage and reliable communication, catering to 6G’s communication requirements. Nevertheless, the swift movement of the LEO satellites poses a challenge: frequent handovers are inevitable, compromising the quality of service (QoS) of users and leading to discontinuous connectivity. Moreover, considering LEO satellite connectivity for different flying vehicles (FVs) when coexisting with ground terminals, an efficient satellite handover decision control and mobility management strategy is required to reduce the number of handovers and allocate resources that align with different user requirements. With the development of machine learning (ML) methods, which can greatly enhance system performance and automation, reinforcement learning (RL), as a sub-field in ML has been employed to optimize decision control. Due to the challenges of dimensionality explosion and the propensity for traditional Q-learning algorithms to get trapped in local minima, deep learning has been introduced with RL. In this thesis, the high-dimensionality user-satellite network is constructed including the LEO constellation from the ephemeris data, different types of flying vehicles such as aircraft and drones, and ground terminals. Two mathematical optimization models named the traditional low handover model and network utility model when considering the full criteria including the remaining visible time, downlink (DL) carrier-to-interference-plus-noise ratio (CINR) and the available idle channels are formulated. In this way, a novel satellite handover strategy based on Multi-Agent Reinforcement Learning (MARL) and game theory named Nash-SAC has been proposed to solve these problems. From the simulation results, compared with different benchmarks such as the traditional Q-learning algorithm, Maximum available channel (MAC)-based strategy, and Maximum instantaneous signal strength (MIS)-based strategy, Nash-SAC can effectively reduce the number of satellite handovers by over 16% close to the lower limit, and the blocking rate by over 18%. Moreover, Nash-SAC can greatly improve the network utility of the whole system by up to 48% and cater to different users’ requirements, providing reliable and robust connectivity for both FVs and ground terminals.