Satellite computing networks leverage the placement of computing resources on low-Earth orbit (LEO) satellite communication network to bring computing power closer to users, enabling robust and scalable solutions for emerging applications such as Internet of thing, edge computing and real-time analysis. However, with the explosion of multi-layer LEO mega-constellations(MLMCs), how to use the least number of computing nodes to achieve computing power resource coverage? This paper proposes a computing node deployment algorithm for MLMCs to construct satellite computing network with the minimum number of computing node. Specifically, we analyze the existence conditions of optimal computing node deployment, and derive the expression of the relationship between the number of computing nodes needed on the LEO, the multi-layer satellite network structure and the number of accessible hops of computing nodes. The expression can determine the minimum number of computing nodes needed to be placed on the MLMCs to realize optimal computing node deployment under a certain number of accessible hops. Then, according to the limitation of optimal computing node deployment, a multi-layer satellite network computing node deployment algorithm is proposed to determine the position of the computing resource in MLMCs, which can form a stable computing structure in satellite network. Finally, through simulation, the influence of network scale and computing node deployment on the number of required computing nodes and the effectiveness of signaling delay is analyzed. The simulation results show that this method can achieve better delay with the least number of computing nodes, balance the number of computing nodes and delay, and meet the specific network requirements.

Low Earth orbit (LEO) mega-constellations equipped with laser inter-satellite links (LISLs) is an important part of future sixth generation (6G). However, how solar background noise affects LEO mega-constellations remains an open research topic. To this regard, this paper first derives the spatio-temporal distribution of affected LISLs in LEO mega-constellations at a specific moment, based on the characteristics of the impact, such as its location and duration. This distribution is then generalized to account for all moments during the Earth's rotation, considering the positional relationship between the LEO mega-constellations and the Sun. Additionally, we define two key metrics: the maximum number of affected LISLs (MNAL) and the affected duration ratio (ADR) to quantify the impact on the constellations. Several examples are presented to show that the MNAL decreases as the phase factor increases and increases with rising inclination. The ADR, on the other hand, increases with the phase factor, but initially increases and then decreases as the inclination rises. This work offers theoretical insights that can guide the design of future LEO mega-constellations.

To deal with the challenges in mobility management of mega low-Earth-orbit (LEO) satellite constellations with long management delays and high signaling overheads, especially under the existing fixed and limited deployments of ground mobility management entities, the cooperative mobility management mode of medium-Earth-orbit (MEO) satellites and ground stations (GSs) has become an attractive tendency. In this paper, considering with the global non-uniform user distribution and constrained satellite storage resources, we propose a dynamic satellite-ground integrated mobility management strategy (DSG-MMS) to cope with the relative mobility among users, GSs, and satellites, which can dynamically decide the optimal GS/MEO management node with the minimal handover and migration delays. Specifically, the DSG-MMS optimization problem is modeled as distributed Markov decision processes, and a reinforcement learning (RL)-based management node selection method is presented to solve them, where each LEO satellite agent dynamically decides its own management node. To further implement the RL algorithm on the resource-limited LEO satellite agents, a novel tensor-based RL algorithm for DSG-MMS is proposed by means of the streamed low-rank tensor decomposition, where only the small-sized core tensor and factor matrices are kept and updated in the strategic optimization so as to realize low storage and computing overheads as well as fast convergence. We perform simulations for the proposed DSG-MMS with parameter configurations of actual Telesat, Kuiper, and Starlink satellite systems to evaluate the mobility management delay and overhead performances as well as the required satellite storage size for mobility management. Moreover, a case study and an architectural comparison are given to demonstrate the superiority of the proposed DSG-MMS than existing methods.

The number of satellites in the current low-Earth-orbit (LEO) satellite networks continues to grow to form a mega LEO satellite constellation (MLSC). This large-scale net-working effectively improves network coverage and capacity. However, denser satellite deployment brings more severe chal-lenges to satellite handovers, such as frequent handovers, which exponentially reduce the system performance (e.g. probability of service success (PSS)) when considering inherent handover failures. To ensure service continuity, this paper focuses on the relationship between the constellation scale and handover times under seamless coverage. Specifically, we first conduct spatial geometric analysis and probabilistic analysis to derive three new conditions for MLSC seamless coverage. Then, we analyze the tradeoff relationships between the handover times and satellite coverage duration with the given constellation scale. Furthermore, we construct a mathematical relationship between the constellation scale, handover times, and PSS, indicating the tradeoff between constellation scale and system performance. The analysis effectively guides to design or adjust the constellation scale, satellite altitude, satellite coverage angle, and handover strategy according to the requirements of system performance, which has important theoretical value for MLSC system design and future research.

The network control plays a vital role in the mega satellite constellation (MSC) to coordinate massive network nodes to ensure the effectiveness and reliability of operations and services for future space wireless communications networks. One of the critical issues in satellite network control is how to design an optimal network control structure (ONCS) by configuring the least number of controllers to achieve efficient control interaction within a limited number of hops. Considering the wide coverage, rising capacity, and no geographical constraints of space platforms, this paper contributes to designing the ONCS by constructing an optimal space control network (SCN) to improve the temporal effectiveness of network control. Specifically, we formulate the optimal SCN construction problem from the perspective of satellite coverage factors, and apply geometric topology analysis to derive both the conditions for constructing the optimal SCN and the formulaic conclusions for SCN and MSC configurations (i.e., scale and structure). From numerical results, we investigate the tradeoff between network scale, the number of controllers, and control delays in several satellite network control scenarios, to provide guidelines for the MSC control. We also design the optimal SCN for an existing MSC system to demonstrate the effectiveness of the proposed ONCS.