Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Abstract

The increasing demand for high-rate broadcast and multicast services over satellite networks has pushed for the development of High Throughput Satellites characterized by a large number of beams (e.g., more than 100). This, together with the variable distribution of data traffic request across beams and over time, has called for the design of a new generation of satellite payloads, able to flexibly allocate bandwidth and power. In this context, this paper studies the problem of radio resource allocation in the forward link of multibeam satellite networks adopting the Digital Video Broadcasting -Satellite -Second Generation (DVB-S2) standard. We propose a novel objective function with the aim to meet as close as possible the requested traffic across the beams while taking fairness into account. The resulting non-convex optimization problem is solved using a modified version of the simulated annealing algorithm, for which a detailed complexity analysis is presented. Simulation results obtained under realistic conditions confirm the effectiveness of the proposed approach and shed some light on possible payload design implications.

AG‐DPA: Assignment game–based dynamic power allocation in multibeam satellite systems

“…The power part k prefers the beam that can provide the maximum utility as Equation 7 while the beam n prefers a list of power parts, which can provide maximum utility as Equation 8. Thus, the most preferred beam for a given power part k can be expressed as Equation 9:…”

“…To fully exploit the potential benefits, DRA should consider not the time‐varying channel condition but the nonuniform traffic among beams. An efficient but simple DRA algorithm is quite important for multibeam satellite systems to fully exploit the on‐board resources, including the power, channels, and caching . As the satellites have very limited energy budget due to the small battery and solar panels, the dynamic power allocation (DPA) technique is quite important to improve the energy efficiency.…”

“…Many research work has studied DPA techniques in multibeam satellite systems . Choi and Chan optimize the power allocation in order to adapt to channel conditions and meet traffic demands through Lagrange multiplier method.…”

“…Further study is taken in Aravanis et al, which proves the NP‐hardness of power allocation with CCI and decomposes the problem into independent color‐based subproblems rendering the proposed two‐stage genetic algorithm and simulated annealing (GA‐SA) metaheuristics algorithm. To avoid the tendency of the SA to local optimum due to the behavior of stochastic optimization algorithm with nonconvex objective functions, Cocco et al proposes a modified version of SA‐based dynamic power allocation algorithm . Although the metaheuristics algorithm (GA or SA) can reach near‐optimal, it heavily depends on the initial parameters and shows slow convergence in large‐scale multibeam satellite systems.…”

On the feasibility of a secondary service transmission over an existent satellite infrastructure: design and analysis

“…It is within this framework that the cognitive radio (CR) techniques have also become attractive for space applications [9]. Based on the recent developments in the space qualified software-defined radios (SDR) [10] and also by the acceptability of concepts such as flexible [11,12] and hosted payloads, these techniques allow a smarter spectrum management. In addition, some valuable research [13] has been acted in spectral awareness and spectral exploitation techniques, which has driven the cognitive satellite communications towards a promising approach.…”