Demand-Aware Cooperative Transmission Design for Energy-Efficient LEO Satellite Networks

2026-08-10

AI Summary

This work addresses the low energy efficiency in low Earth orbit satellite networks caused by spatially non-uniform traffic demands and stringent on-board power constraints. To tackle this challenge, the authors propose a traffic-aware cooperative transmission framework that organizes satellites into user-centric clusters and jointly optimizes the cooperative architecture and resource allocation. The framework integrates hybrid precoding, RF chain activation, hardware quantization, and statistical channel state information. For the first time, it unifies the modeling of heterogeneous traffic demands and network-wide energy consumption, and introduces a distributed two-stage algorithm to solve the resulting high-dimensional mixed-integer nonlinear problem. Experimental results demonstrate that the proposed scheme significantly improves energy efficiency while meeting quality-of-service requirements, outperforming existing benchmarks and effectively balancing traffic heterogeneity with system-wide energy efficiency.

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.

Problem

• LEO satellite networks
• non-uniform traffic demand
• energy efficiency
• power constraints
• cooperative transmission

Innovation

• demand-aware
• cooperative transmission
• energy efficiency
• hybrid precoding
• low Earth orbit (LEO) satellite