A Low-complexity Resource Optimization Technique for High Throughput Satellite

The high throughput satellites with flexible payloads
are expected to provide a high data rate to satisfy the increasing
traffic demand. Furthermore, the reconfiguration capability of
flexible payloads opens the door to more advanced system
optimization techniques and a better utilization of satellite
resources. Consequently, we can obtain high demand satisfaction
at the user side. For this, dynamically adaptive high-performance
and low-complexity optimization algorithms are needed. In this
paper, we propose a novel low-complexity resource optimization
technique for geostationary (GEO) High Throughput Satellites.
The proposed method minimizes the transmit power and the
overall satellite bandwidth while satisfying the demand per beam.
This optimization problem turns out to be non-convex. Hence, we
convexify the problem using Dinkelbach method and Successive
Convex Approximation (SCA). The simulation result shows
that the proposed scheme provides better flexibility in resource
allocation and requires less computational time compared to the
state-of-art benchmark schemes.