Load-Adaptive and Energy-Efficient Topology Control in LEO Mega-Constellation Networks

The Low-Earth-Orbit (LEO) mega-constellation networks, by providing low-latency and high-speed communications, are becoming indispensable infrastructures for the future six-generation (6G) architecture. Consequently, the topology, with thousands of satellites equipped with batteries of limited life, has to be adaptively controlled with high energy efficiency. However, existing work lacks the joint consideration of energy efficiency and load adaptation. In this paper, we first propose the line-of-sight condition to determine the candidate ISL set. Next, we model the energy consumption of the LEO mega-constellation networks. Along this direction, we formulate the Load-Adaptive and Energy-Efficient (LAEE) topology control problem in LEO mega-constellation networks and prove its NP-hardness. Finally, we propose the Amortized Energy based Topology Control (AETC) algorithm to solve the LAEE problem, with good adaptation to the fluctuating load and guarantees connectivities between any two satellites. Extensive simulation results demonstrate that the AETC algorithm outperforms related schemes in terms of energy consumption and results in good topology stability.

I. Introduction

The Low-Earth-Orbit (LEO) mega-constellation networks, with more than 10 times the number of satellites in orbit today, have been widely studied in recent years [1]. Compared with traditional constellations such as the Iridium system, the LEO mega-constellation networks are believed to be indispensable infrastructures for the six-generation (6G) and satellite based Internet of Things architecture in the foreseeable future [2], [3]. However, the limited number of complete discharge and charge battery cycles of the on-orbit satellites challenges the mega-constellation design, for example, it will be a massive device update cost for Starlink with eventually more than 40,000 nodes. Besides, complex link switches and fluctuating loads further complicate the topology control. Hence, realizing energy-efficient topology control with good adaptation to the varying load is a critical but difficult problem [4], [5].