Metasurface Antenna-Enabled LEO Satellite Constellation Communications: Design and Optimization

2026-07-14

AI Summary

This work addresses the bottlenecks in spectral efficiency and onboard hardware complexity faced by low Earth orbit (LEO) satellite constellations by introducing metasurface antennas into LEO satellite communications for the first time. The authors propose a mixed-integer nonlinear optimization framework that jointly optimizes user scheduling and passive beamforming. Leveraging an alternating optimization strategy, the approach employs minimum-cost maximum-flow (MCMF) to achieve polynomial-time-complexity user scheduling and integrates weighted minimum mean square error (WMMSE) with semidefinite relaxation (SDR) to design high-precision beamforming patterns that effectively suppress multiuser interference. Simulation results demonstrate that the proposed method significantly enhances both the system’s weighted sum rate and resource utilization efficiency.

Abstract

Next-generation low Earth orbit (LEO) satellite constellations face critical bottlenecks in spectral efficiency and onboard hardware complexity. To overcome these limitations, this paper introduces a novel architecture enabled by metasurface antennas (MAs) at the LEO satellites. In particular, MAs are metasurface-integrated feed antennas that perform high-precision beamforming directly in the wave domain, thereby effectively mitigating multi-user interference. Based on such an antenna architecture, a weighted sum rate (WSR) maximization problem is formulated by jointly optimizing the scheduling of feed antennas to terrestrial users (TUs) and the passive beamforming of the metasurface for system performance enhancement. To address this mixed-integer nonlinear programming (MINLP) challenge, an alternating optimization (AO)-based joint scheduling and beamforming algorithm is proposed. On the one hand, the proposed algorithm incorporates a polynomial-time minimum-cost maximum-flow (MCMF) method, which is dedicated to the optimal scheduling of feed antennas and TUs. On the other hand, it adopts a weighted minimum mean square error (WMMSE) method integrated with semidefinite relaxation (SDR) technique, which is tailored for metasurface beamforming design. Simulation results confirm the effectiveness of the proposed algorithm for MA-enabled LEO satellite constellation communications.

Problem

• LEO satellite constellation
• spectral efficiency
• hardware complexity
• metasurface antennas
• multi-user interference

Innovation

• metasurface antenna
• LEO satellite constellation
• beamforming
• alternating optimization
• weighted sum rate