  Non-terrestrial network (NTN) communication has garnered considerable
attention from government entities, industries, and academia in recent times.
NTN networks encompass a variety of systems, including Low Earth Orbit (LEO)
satellites, Medium Earth Orbit (MEO) satellites, Geostationary Earth Orbit
(GEO) satellites, High Altitude Platforms (HAPS), and Low Altitude Platforms
(LAPS). Furthermore, the deployment of high-throughput satellites (HTS/VHTS) in
the GEO space has gained momentum. While LEO and MEO satellites offer
advantages such as low latency and reduced launching costs compared to GEO
satellites, this study focuses on GEO satellites due to their stationary nature
and broader coverage. In traditional cellular networks, each user equipment
(UE) is allocated at least one resource block (RB), which is not shared with
other UEs. However, in NTN communications, where the coverage area is
extensive, dedicating an RB to only one UE is an inefficient utilization of
radio resources. To address this challenge, fractional programming (FP),
cognitive radio, and rate splitting multiple access (RSMA) are existing
technologies. This paper aims to maximize spectral efficiency, average RBG
rate, and sum rate for GEO satellite systems. However, achieving this objective
involves dealing with a non-convex, NP-hard problem, as it requires the
logarithmic sum of different fractions. Finding a global solution to such an
NP-hard problem presents significant challenges. This paper introduces a novel
alternate fractional programming algorithm specifically designed to tackle
these complex NP-hard problems in the context of GEO NTN cellular networks. By
employing this innovative approach, the study seeks to contribute to the
optimization of NTN communication systems, enabling efficient resource
allocation and improved network performance.
