User Scheduling and Power Allocation for Precoded Multi-Beam High Throughput Satellite Systems with Individual Quality of Service Constraints

TL;DR

This paper introduces a heuristic user scheduling and power allocation method for multi-beam high throughput satellite systems, improving QoS guarantees and system throughput while managing computational complexity.

Contribution

It proposes a novel joint user scheduling and power allocation algorithm with a geometric programming-based solution for MB-HTS systems with QoS constraints.

Findings

Enhanced QoS satisfaction in large-scale systems

Improved overall system throughput

Effective heuristic algorithm balancing performance and complexity

Abstract

For extensive coverage areas, multi-beam high throughput satellite (MB-HTS) communication is a promising technology that plays a crucial role in delivering broadband services to many users with diverse Quality of Service (QoS) requirements. This paper focuses on MB-HTS systems where all beams reuse the same spectrum. In particular, we propose a novel user scheduling and power allocation design capable of providing guarantees in terms of the individual QoS requirements while maximizing the system throughput under a limited power budget. Precoding is employed in the forward link to mitigate mutual interference at the users in multiple-access scenarios over different coherence time intervals. The combinatorial optimization structure from user scheduling requires an extremely high cost to obtain the global optimum even when a reduced number of users fit into a time slot. Therefore, we propose a heuristic algorithm yielding good trade-off between performance and computational complexity, applicable to a static operation framework of geostationary (GEO) satellite networks. Although the power allocation optimization is signomial programming, non-convex on a standard form, the solution can be lower bounded by the global optimum of a geometric program with a hidden convex structure. A local solution to the joint user scheduling and power allocation problem is consequently obtained by a successive optimization approach. Numerical results demonstrate the effectiveness of our algorithms on large-scale systems by providing better QoS satisfaction combined with outstanding overall system throughput.