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Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms.

Zhou, G., Zhao, L., Zheng, G., Song, S., Zhang, J. and Hanzo, L., 2024. Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms. IEEE Internet of Things Journal, 11 (5), 8327-8344.

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DOI: 10.1109/JIOT.2023.3319130

Abstract

As an attractive enabling technology for nextgeneration wireless communications, network slicing supports diverse customized services in the global space-air-ground integrated network (SAGIN) with diverse resource constraints. In this paper, we dynamically consider three typical classes of radio access network (RAN) slices, namely high-throughput slices, lowdelay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay and the coverage area of these three classes of RAN slices are jointly optimized in a non-scalar form by considering the distinct channel features and service advantages of the terrestrial, aerial and satellite components of SAGINs. A joint central and distributed multi-agent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the inter-slice sub-channel and power sharing by relying on a centralized unit. Then it optimizes the intra-slice sub-channel and power allocation, and the virtual base station (vBS)/vUAV/virtual low earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Paretooptimal exploitation of multiple RAN slices, and outperforms the benchmarkers.

Item Type:Article
ISSN:2327-4662
Additional Information:“© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Uncontrolled Keywords:Radio access network slicing; space-airground integrated network; multi-objective optimization; nonscalarization; hierarchical and distributed deep reinforcement learning
Group:Faculty of Science & Technology
ID Code:39007
Deposited By: Symplectic RT2
Deposited On:04 Oct 2023 16:03
Last Modified:05 Jun 2024 09:27

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