User-Centric Flexible Resource Management Framework for LEO Satellites With Fully Regenerative Payload

BHANDARI, Sovit; VU, Thang Xuan; CHATZINOTAS, Symeon

doi:10.1109/JSAC.2024.3365883

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User-Centric Flexible Resource Management Framework for LEO Satellites With Fully Regenerative Payload

BHANDARI, Sovit; VU, Thang Xuan; CHATZINOTAS, Symeon

2024 • In IEEE Journal on Selected Areas In Communications, 42 (5), p. 1246 - 1261

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https://hdl.handle.net/10993/62188

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10.1109/JSAC.2024.3365883

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Keywords :

beamforming; caching; deep learning; handover; LEO satellite; multicasting; optimization; precoding; regenerative payload; Caching; Deep learning; Hand over; Low earth orbit satellites; Optimisations; Payload; Phased-arrays; Precoding; Regenerative payloads; Satellite broadcasting; Computer Networks and Communications; Electrical and Electronic Engineering

Abstract :

[en] The regenerative capabilities of next-generation satellite systems offer a novel approach to design low earth orbit (LEO) satellite communication systems, enabling full flexibility in bandwidth and spot beam management, power control, and onboard data processing. These advancements allow the implementation of intelligent spatial multiplexing techniques, addressing the ever-increasing demand for future broadband data traffic. Existing satellite resource management solutions, however, do not fully exploit these capabilities. To address this issue, a novel framework called flexible resource management algorithm for LEO satellites (FLARE-LEO) is proposed to jointly design bandwidth, power, and spot beam coverage optimized for the geographic distribution of users. It incorporates multi-spot beam multicasting, spatial multiplexing, caching, and handover (HO). In particular, the spot beam coverage is optimized by using the unsupervised K-means algorithm applied to the realistic geographical user demands, followed by a proposed successive convex approximation (SCA)-based iterative algorithm for optimizing the radio resources. Furthermore, we propose two joint transmission architectures during the HO period, which jointly estimate the downlink channel state information (CSI) using deep learning and optimize the transmit power of the LEOs involved in the HO process to improve the overall system throughput. Simulations demonstrate superior performance in terms of delivery time reduction of the proposed algorithm over the existing solutions.

Disciplines :

Electrical & electronics engineering

Author, co-author :

BHANDARI, Sovit ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

VU, Thang Xuan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

External co-authors :

Language :

English

Title :

User-Centric Flexible Resource Management Framework for LEO Satellites With Fully Regenerative Payload

Publication date :

14 February 2024

Journal title :

IEEE Journal on Selected Areas In Communications

ISSN :

0733-8716

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Special issue title :

Space Communications New Frontiers: From Near Earth to Deep Space

Volume :

Issue :

Pages :

1246 - 1261

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx7/49/10529142/10436159.pdf?arnumber=10436159

FnR Project :

FNR14016225 - Integrated Satellite-terrestrial Systems For Ubiquitous Beyond 5g Communications, 2020 (01/10/2020-30/09/2026) - Symeon Chatzinotas

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since 09 October 2024

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