Hybridisation Schemes for Communication Satellite Payload Configuration Optimisation

International audienceThe increasing complexity of current telecommunication satellite payloads has made their manual management a difficult and error prone task. As a consequence, efficient optimisation techniques are re- quired to help engineers to configure the payload. Recent works focusing on exact approaches faced scalability issues while metaheuristics provided unsatisfactory solution quality. This work therefore proposes three hybridisation schemes that combine both metaheuristics and an exact method. We focus on the initial configuration problem case and we consider as objective to minimise the length of the longest channel path. Experimental results on realistic payload sizes demonstrate the advantage of those approaches in terms of efficiency within a strict operational time constraint of ten minutes on a single CPU core

Multi-objective evolutionary approach for the satellite payload power optimization problem

International audienceToday’s world is a vast network of global communications systems in which satellites provide high-performance and long distance communications. Satellites are able to forward signals after amplification to offer a high level of service to customers. These signals are composed of many differentchannel frequencies continuously carrying real-time data feeds. Nevertheless, the increasing demands of the market force satellite operators to develop efficient approaches to manage satellite configurations, in which power transmission is one crucial criterion. Not only the signal power sent to the satellite needs to be optimal to avoid large costs but also the power of the downlink signal has to be strong enough to ensure the quality of service. In this work, we tackle for the first time the bi-objective input/output power problem with multi-objective evolutionary algorithms to discover efficient solutions. A problem specific indirect encoding is proposed and the performance of three state-of-the-art multiobjective evolutionary algorithms, i.e. NSGA-II, SPEA2 and MOCell, is compared on real satellite payload instances

Bi-objective Exact Optimization of Satellite Payload Power Configuration

Abstract-Communications satellites are designed to provide services by forwarding signals to customers. Uplink signals are filtered and amplified to ensure signal output quality. This is ensured by the payload part of the satellite. Reconfigurable hardware components like switches are embedded in the payload to route signals through the satellite. By setting switch positions, satellite engineers are able to connect, restore or reconfigure channels. However, to follow the increasing demands, satellite payloads embed more and more components. As a consequence, their manual management is becoming time-consuming and error-prone. Power transmission optimization has then a crucial role to decrease costs while keeping a maximum quality of service. In this aim, satellite operators would like to minimize the power of the signals sent from Earth while keeping a maximum amplification. In this work, we tackle for the first time this new bi-objective problem of optimizing input and output power. Exact methods have been considered to propose efficient alternatives to the satellite operators