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

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 sta...

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Bibliographic Details
Published in:IEEE wireless communications letters Vol. 9; no. 7; pp. 1101 - 1104
Main Authors: Efrem, Christos N., Panagopoulos, Athanasios D.
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Availability
binary integer linear programming
branch-and-bound method
cardinality minimization
combinatorial optimization
Communications systems
Complexity theory
Geographical distribution
Ground stations
ground stations selection
linear-programming relaxation
NP-hardness
Optical fiber networks
Optimization
Optimized production technology
Probability
Radio frequency
Satellite broadcasting
Satellite communications
Satellites
site diversity
ISSN:2162-2337, 2162-2345
Online Access:Get full text
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Summary: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.
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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2020.2982139