Dynamic Frequency Assignment for Mobile Users in Multibeam Satellite Constellations

TL;DR

A dynamic frequency management algorithm based on linear programming that assigns resources in scenarios with both fixed and mobile users by combining long-term planning with real-time operation is proposed, divided into proactive strategies, which stem from robust optimization practices, and reactive strategies, which exploit a high degree of real-time control.

Abstract

Mobile users such as airplanes or ships will constitute an important segment of the future satellite communications market. Operators are now able to leverage digital payloads that allow flexible resource allocation policies that are robust against dynamic user bases. One of the key problems is managing the frequency spectrum efficiently, which has not been sufficiently explored for mobile users. To address this gap, we propose a dynamic frequency management algorithm based on linear programming that assigns resources in scenarios with both fixed and mobile users by combining long-term planning with real-time operation. We propose different strategies divided into proactive strategies, which stem from robust optimization practices, and reactive strategies, which exploit a high degree of real-time control. This represents a tradeoff between how conservative long-time planning should be and how much real-time reconfiguration is needed. To assess the performance of our method and to determine which proactive and reactive strategies work better under which context, we simulate operational use cases of non-geostationary constellations with different levels of dimensionality and uncertainty, showing that our method is able to serve over 99.97\% of the fixed and mobile users in scenarios with more than 900 beams. Finally, we discuss the trade-offs between the studied strategies in terms of the number of served users, power consumption, and number of changes that need to happen during operations.

Figures (8)

• Figure 1: NGSO constellation plane with $N_S$ satellites connecting fixed and mobile users to gateways and frequency assignment representation in the form of a grid with $N_{r}\cdot N_p$ rows (frequency groups) and $N_{ch}$ columns (frequency channels). In this example, $N_{r} = 3$, $N_p = 2$, and $N_{ch} = 7$. In this case, the allocation in blue is defined by $f=4$, $b=4$, $g=1$, $p=2$.
• Figure 2: User distribution and frequency assignment of two satellites at two different instances. Contrary to fixed users, the change in position of the mobile user in green prompts a change in the frequency allocation (highlighted in a darker color).
• Figure 3: Proposed frequency assignment framework, consisting of a proactive assignment stage that generates a baseline plan accounting for uncertainty before operations, and a reactive assignment stage that re-optimizes the plan during operations when new information is available.
• Figure 4: Frequency assignment (no uncertainty, $U_{info}=U_{tot}$) at time 9h40min. The upper part shows the users' positions along their trajectories, where the shaded areas are the beam footprints. The lower part shows the frequency assignment, highlighting the frequency group plotted in Figure \ref{['fig:r_rg_plan']}.
• Figure 5: Baseline frequency plan with no uncertainty ($U_{info}=U_{tot}$) from the perspective of the satellites (frequency group 2) and one of the gateways. The horizontal axis shows the simulation time $t\in[0,T]$, with $T=24$h, and the vertical axis shows the frequency channels, with $N_{ch}=80$. The dashed vertical line indicates the instance 9h40min, for which the constellation assignment is plotted in Figure \ref{['fig:r_constellation_plan']}.