QoE-Aware Cost-Minimizing Capacity Renting for Satellite-as-a-Service enabled Multiple-Beam SatCom Systems

The advent of Satellite as a Service (SaaS) platforms
has empowered satellite service providers (SPs) to rent portions
of satellite capacity from infrastructure providers (IPs) to cater
to the diverse demands of their users across multiple satellite
services. To effectively manage costs and maintain a high Quality
of Experience (QoE) for numerous concurrent connections,
SPs should secure flexible capacity from IPs. However, the
irregular and unpredictable nature of traffic demands from
various applications complicates the capacity-renting framework.
This study presents a dynamic capacity allocation framework
that efficiently handles diverse traffic flows with varying arrival
rates, aiming to minimize rental costs while meeting blocking
probability and QoE requirements. Utilizing the 𝑀𝑡 /𝑀𝑡 /1 queuing
model and a continuous-time Markov chain, the technical
designs are framed as a statistical optimization problem. In
this context, the system waiting-queue lengths are estimated using
the transient probabilities of Kolmogorov equations. Subsequently,
cumulative distribution functions are employed to re-formulate
this stochastic optimization problem into a convex form, which
can be tackled through the Lagrangian duality method.Through
extensive simulations and numerical assessments, we illustrate
our method’s efficacy, with the proposed algorithm outperforming
benchmarks by reducing costs by up to 9.85% and 3.1%.