Supervised Learning Based Real-Time Adaptive Beamforming On-board Multibeam Satellites

TL;DR

This paper presents a real-time adaptive beamforming method using supervised learning for multibeam satellites, enhancing resource management and QoS in satellite communications with a novel on-board algorithm.

Contribution

It introduces a supervised learning-based algorithm for on-board adaptive beamforming in multibeam satellites, enabling dynamic beam adjustments in real-time.

Findings

Achieved precise beam projection and dynamic beamwidth adjustment.

Minimized sidelobe levels and optimized EIRP.

Demonstrated effectiveness in a satellite communication context.

Abstract

Satellite communications (SatCom) are crucial for global connectivity, especially in the era of emerging technologies like 6G and narrowing the digital divide. Traditional SatCom systems struggle with efficient resource management due to static multibeam configurations, hindering quality of service (QoS) amidst dynamic traffic demands. This paper introduces an innovative solution - real-time adaptive beamforming on multibeam satellites with software-defined payloads in geostationary orbit (GEO). Utilizing a Direct Radiating Array (DRA) with circular polarization in the 17.7 - 20.2 GHz band, the paper outlines DRA design and a supervised learning-based algorithm for on-board beamforming. This adaptive approach not only meets precise beam projection needs but also dynamically adjusts beamwidth, minimizes sidelobe levels (SLL), and optimizes effective isotropic radiated power (EIRP).