Open Access

ARTICLE

Dynamic Resource Pricing and Allocation in Multilayer Satellite Network

Yuan Li^1,7, Jiaxuan Xie¹, Mu Xia², Qianqian Li³, Meng Li⁴, Lei Guo^5,*, Zhen Zhang⁶

1 Tsinghua University, Beijing, 100084, China
2 School of Automation, Northwestern Polytechnical University, Xi’an, 710129, China
3 University of Science and Technology Beijing, Beijing, 100083, China
4 Beijing University of Posts and Telecommunications, Beijing, 100876, China
5 Systems Engineering Institute AMS, Beijing, 100071, China
6 Audio Analytic, 2 Quayside, Cambridge, CB5 8AB, UK
7 Beijing Commsat Technology Development Co., Ltd., Beijing, 100089, China

* Corresponding Author: Lei Guo. Email:

Computers, Materials & Continua 2021, 69(3), 3619-3628. https://doi.org/10.32604/cmc.2021.016187

Received 26 December 2020; Accepted 07 April 2021; Issue published 24 August 2021

Abstract

The goal of delivering high-quality service has spurred research of 6G satellite communication networks. The limited resource-allocation problem has been addressed by next-generation satellite communication networks, especially multilayer networks with multiple low-Earth-orbit (LEO) and non-low-Earth-orbit (NLEO) satellites. In this study, the resource-allocation problem of a multilayer satellite network consisting of one NLEO and multiple LEO satellites is solved. The NLEO satellite is the authorized user of spectrum resources and the LEO satellites are unauthorized users. The resource allocation and dynamic pricing problems are combined, and a dynamic game-based resource pricing and allocation model is proposed to maximize the market advantage of LEO satellites and reduce interference between LEO and NLEO satellites. In the proposed model, the resource price is formulated as the dynamic state of the LEO satellites, using the resource allocation strategy as the control variable. Based on the proposed dynamic game model, an open-loop Nash equilibrium is analyzed, and an algorithm is proposed for the resource pricing and allocation problem. Numerical simulations validate the model and algorithm.

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