Decentralized Cooperative Beamforming for Networked LEO Satellites with Statistical CSI

TL;DR

This paper introduces a decentralized cooperative beamforming framework for LEO satellite networks, enabling scalable, efficient, and near-centralized performance without heavy signaling or computation.

Contribution

It develops a topology-agnostic decentralized algorithm with low complexity, suitable for large LEO satellite constellations, improving scalability and performance.

Findings

Decentralized schemes closely match centralized performance.

Significant reduction in computational complexity and signaling overhead.

Algorithm is fully parallelizable and scalable for large constellations.

Abstract

Inter-satellite-link-enabled low-Earth-orbit (LEO) satellite constellations are evolving toward networked architectures that support constellation-level cooperation, enabling multiple satellites to jointly serve user terminals through cooperative beamforming. While such cooperation can substantially enhance link budgets and achievable rates, its practical realization is challenged by the scalability limitations of centralized beamforming designs and the stringent computational and signaling constraints of large LEO constellations. This paper develops a fully decentralized cooperative beamforming framework for networked LEO satellite downlinks. Using an ergodic-rate-based formulation, we first derive a centralized weighted minimum mean squared error (WMMSE) solution as a performance benchmark. Building on this formulation, we propose a topology-agnostic decentralized beamforming algorithm by localizing the benchmark and exchanging a set of globally coupled variables whose dimensions are independent of the antenna number and enforcing consensus over arbitrary connected inter-satellite networks. The resulting algorithm admits fully parallel execution across satellites. To further enhance scalability, we eliminate the consensus-related auxiliary variables in closed form and derive a low-complexity per-satellite update rule that is optimal to local iteration and admits a quasi-closed-form solution via scalar line search. Simulation results show that the proposed decentralized schemes closely approach centralized performance under practical inter-satellite topologies, while significantly reducing computational complexity and signaling overhead, enabling scalable cooperative beamforming for large LEO constellations.