Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI

The densification of low-Earth orbit (LEO) constellations and the development of inter-satellite links enable the cooperative transmission of multiple satellites. This paper investigates a decentralized resource allocation strategy for multi-satellite cooperation networks under statistical channel s...

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Main Authors: Meihui Zhao, Hanxiao Yu, Jianxiong Pan, Yifeng Jin, Guocheng Lv, Ye Jin
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10409505/
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author Meihui Zhao
Hanxiao Yu
Jianxiong Pan
Yifeng Jin
Guocheng Lv
Ye Jin
author_facet Meihui Zhao
Hanxiao Yu
Jianxiong Pan
Yifeng Jin
Guocheng Lv
Ye Jin
author_sort Meihui Zhao
collection DOAJ
description The densification of low-Earth orbit (LEO) constellations and the development of inter-satellite links enable the cooperative transmission of multiple satellites. This paper investigates a decentralized resource allocation strategy for multi-satellite cooperation networks under statistical channel state information (CSI). Our formulation aims at minimizing the total matching error between non-uniform traffic requests and achievable throughput by jointly optimizing transmit power and beam illumination pattern. The original problem is a stochastic mixed-integer quadratic programming with the long-term accumulation nature, whose stochastic objective is first addressed by incorporating the outage probability constraint. Then we decompose the deterministic problem into a series of single-timeslot subproblems on the greedy basis, each of which is further decomposed for each satellite following the decentralized decision principle with a limited number of inter-satellite interactions required to obtain the overall resource allocation. Finally, the optimization problem at each satellite is solved by sequential quadratic programming (SQP)-based alternating optimization algorithm that iterates between the transmit power allocation and beam illumination design. Numerical results show the effectiveness of the proposed decentralized algorithm in reducing the total matching error by at least 27% over other baseline approaches.
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spelling doaj.art-0905f298f62e439bb08545bc11ebcb0c2024-02-02T00:02:02ZengIEEEIEEE Access2169-35362024-01-0112154191543710.1109/ACCESS.2024.335605810409505Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSIMeihui Zhao0https://orcid.org/0000-0003-1922-2968Hanxiao Yu1https://orcid.org/0000-0003-3399-8359Jianxiong Pan2https://orcid.org/0000-0002-6969-1407Yifeng Jin3https://orcid.org/0000-0002-2343-7139Guocheng Lv4https://orcid.org/0000-0002-7136-3402Ye Jin5School of Electronics, Peking University, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaChina Academy of Space Technology, Institute of Telecommunication and Navigation Satellites, Beijing, ChinaSchool of Electronics, Peking University, Beijing, ChinaSchool of Electronics, Peking University, Beijing, ChinaThe densification of low-Earth orbit (LEO) constellations and the development of inter-satellite links enable the cooperative transmission of multiple satellites. This paper investigates a decentralized resource allocation strategy for multi-satellite cooperation networks under statistical channel state information (CSI). Our formulation aims at minimizing the total matching error between non-uniform traffic requests and achievable throughput by jointly optimizing transmit power and beam illumination pattern. The original problem is a stochastic mixed-integer quadratic programming with the long-term accumulation nature, whose stochastic objective is first addressed by incorporating the outage probability constraint. Then we decompose the deterministic problem into a series of single-timeslot subproblems on the greedy basis, each of which is further decomposed for each satellite following the decentralized decision principle with a limited number of inter-satellite interactions required to obtain the overall resource allocation. Finally, the optimization problem at each satellite is solved by sequential quadratic programming (SQP)-based alternating optimization algorithm that iterates between the transmit power allocation and beam illumination design. Numerical results show the effectiveness of the proposed decentralized algorithm in reducing the total matching error by at least 27% over other baseline approaches.https://ieeexplore.ieee.org/document/10409505/Multi-satellite cooperation networkstatistical CSImixed-integer quadratic programmingstochastic objectivedecentralized resource allocation
spellingShingle Meihui Zhao
Hanxiao Yu
Jianxiong Pan
Yifeng Jin
Guocheng Lv
Ye Jin
Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI
IEEE Access
Multi-satellite cooperation network
statistical CSI
mixed-integer quadratic programming
stochastic objective
decentralized resource allocation
title Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI
title_full Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI
title_fullStr Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI
title_full_unstemmed Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI
title_short Dynamic Resource Allocation for Multi-Satellite Cooperation Networks: A Decentralized Scheme Under Statistical CSI
title_sort dynamic resource allocation for multi satellite cooperation networks a decentralized scheme under statistical csi
topic Multi-satellite cooperation network
statistical CSI
mixed-integer quadratic programming
stochastic objective
decentralized resource allocation
url https://ieeexplore.ieee.org/document/10409505/
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