Resource optimization in multi-beam satellites

Abstract

Multi-beam satellite systems offer higher throughput due to frequency reuse and multiple levels of flexibility and allow the use of smaller earth terminals due to their higher directivity. At a high-level multi-beam satellite operation resembles to that of cellular mobile communications. As the lifetime of a satellite is about 15 years efficient communication satellites must take into account current as well as future demands. To improve satellite efficiency and reduce operational risk, design flexibility is imperative. Among others, multibeam satellites, offer significant flexibility both with regard to coverage area and resources allocation. Coverage area flexibility is achieved by adjusting the position of the spots adaptively to communications traffic. Resource allocation flexibility is made possible by adaptively adjusting the power and bandwidth to the various beams according to traffic. In the present thesis, we focus on resource allocation flexibility offered by multi-beam satellites. In particular, for a specific satellite payload (number of beams, available power, bandwidth etc.,) the first part of the thesis deals with the problem of allocating power to different beams to satisfy the demand as closely as possible. Based on a model relating the power allocated to the data rate offered, we explore the suitability of using/ modifying existing optimization algorithms. Among exact methods as well as approximate methods that are initially considered, a scheme is selected based on complexity, convergence, scalability and other issues. The selected algorithm is then used to optimize the resource allocation. The advantages obtained over other existing methods are presented. Next, an attempt is made to minimize the DC power consumption of a multi-beam satellite. In this course, the optimized resource allocation obtained in the first part of the thesis is further enhanced, with the aim to minimize the system power consumption. This task is deal with employing a multi-objective optimization algorithm that aims at meeting the traffic demand as closely as possible while minimizing the system power consumption. The benefits from using such multiple.