Multibeam High Throughput Satellite: Hardware Foundation, Resource Allocation, and Precoding

TL;DR

This paper reviews the hardware, resource allocation, and precoding techniques of multibeam high throughput satellite systems, highlighting challenges and future research directions to improve satellite communication capacity for next-generation networks.

Contribution

It provides a comprehensive overview of multibeam HTS hardware, resource management strategies, and precoding methods, and discusses key challenges and future perspectives in the field.

Findings

Summarizes multibeam HTS hardware components.

Reviews flexible resource allocation approaches.

Classifies and analyzes multibeam precoding methods.

Abstract

With its wide coverage and uninterrupted service, satellite communication is a critical technology for next-generation 6G communications. High throughput satellite (HTS) systems, utilizing multipoint beam and frequency multiplexing techniques, enable satellite communication capacity of up to Tbps to meet the growing traffic demand. Therefore, it is imperative to review the-state-of-the-art of multibeam HTS systems and identify their associated challenges and perspectives. Firstly, we summarize the multibeam HTS hardware foundations, including ground station systems, on-board payloads, and user terminals. Subsequently, we review the flexible on-board radio resource allocation approaches of bandwidth, power, time slot, and joint allocation schemes of HTS systems to optimize resource utilization and cater to non-uniform service demand. Additionally, we survey multibeam precoding methods for the HTS system to achieve full-frequency reuse and interference cancellation, which are classified according to different deployments such as single gateway precoding, multiple gateway precoding, on-board precoding, and hybrid on-board/on-ground precoding. Finally, we disscuss the challenges related to Q/V band link outage, time and frequency synchronization of gateways, the accuracy of channel state information (CSI), payload light-weight development, and the application of deep learning (DL). Research on these topics will contribute to enhancing the performance of HTS systems and finally delivering high-speed data to areas underserved by terrestrial networks.