Demand-Aware Cooperative Transmission Design for Energy-Efficient LEO Satellite Networks
2026-08-10 • Information Theory
Information Theory
AI summaryⓘ
The authors explore how networks of Low Earth Orbit (LEO) satellites can provide internet access by handling different user demands and saving power. They propose a method where satellites work together, using smart signal techniques and selecting which hardware parts to use, based on where demand is high or low. They create a complex optimization system to balance energy efficiency and user needs, then develop an algorithm to solve it effectively. Their tests show this approach works better than existing methods by considering both traffic needs and energy use.
Low Earth Orbit (LEO) satellitessatellite constellationshybrid precodingradio frequency (RF) chain activationhardware quantizationuser-equipment (UE)-centric clusteringstatistical channel state information (sCSI)demand-aware energy efficiencymixed-integer nonlinear programming (MINLP)fractional programming
Authors
Wooseok Cha, Kyeongsoo Kim, Seonghoon Kim, Junil Choi, Jihwan P. Choi
Abstract
Low Earth orbit (LEO) satellite networks are envisioned as a promising solution for providing ubiquitous connectivity and narrowing the digital divide. The extensive footprint of LEO satellite constellations enables broad coverage, resulting in spatially non-uniform traffic demand across the serviced areas. Meanwhile, stringent on-board power constraints make power-intensive transmission architectures less attractive and motivate energy-efficient transmission strategies that effectively exploit scarce satellite network resources. To this end, this paper proposes a cooperative transmission framework that jointly accounts for non-uniform traffic demand and network-wide power consumption. Each LEO satellite integrates hybrid precoding (HPC), radio frequency (RF) chain activation, and hardware quantization, while user-equipment (UE)-centric satellite clusters are organized using statistical channel state information (sCSI) and traffic demands. A framework for joint optimization of cooperative transmission architecture and resource allocation is designed to maximize demand-aware energy efficiency (EE), resulting in a mixed-integer nonlinear program (MINLP) for which finding a globally optimal solution is generally intractable. Accordingly, a two-stage algorithm is developed under a distributed linear precoding structure, in which a modified cross-entropy (CE) method searches over discrete variables, while fractional programming is employed for transmit power allocation. Numerical results indicate that the proposed framework outperforms benchmark schemes while accounting for traffic demands and EE.