Globally Optimal Selection of Ground Stations in Satellite Systems with Site Diversity

TL;DR

This paper presents a globally optimal method for selecting the minimum number of ground stations in satellite systems with site diversity, ensuring high availability despite atmospheric impairments.

Contribution

It formulates the GS selection as an NP-hard binary-integer-linear-programming problem and proposes a branch-and-bound algorithm with proven global optimality.

Findings

The proposed algorithm outperforms existing methods in accuracy.

It achieves near-optimal solutions with low average complexity.

Numerical results validate the effectiveness of the approach.

Abstract

The availability of satellite communication systems is extremely limited by atmospheric impairments, such as rain (for radio frequencies) and cloud coverage (for optical frequencies). A solution to this problem is the site diversity technique, where a network of geographically distributed ground stations (GSs) can ensure, with high probability, that at least one GS is available for connection to the satellite at each time period. However, the installation of redundant GSs induces unnecessary additional costs for the network operator. In this context, we study an optimization problem that minimizes the number of required GSs, subject to availability constraints. First, the problem is transformed into a binary-integer-linear-programming (BILP) problem, which is proven to be NP-hard. Subsequently, we design a branch-and-bound (B&B) algorithm, with global-optimization guarantee, based on the linear-programming (LP) relaxation and a greedy method as well. Finally, numerical results show that the proposed algorithm significantly outperforms state-of-the-art methods, and has low complexity in the average case.