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

Authors: Zhou, G., Zhao, L., Zheng, G., Song, S., Zhang, J., Hanzo, L.

Journal: IEEE Internet of Things Journal

Publication Date: 01/03/2024

Volume: 11

Issue: 5

Pages: 8327-8344

eISSN: 2327-4662

DOI: 10.1109/JIOT.2023.3319130

Abstract:

As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space-air-ground-integrated network (SAGIN) with diverse resource constraints. In this article, we dynamically consider three typical classes of radio access network (RAN) slices, namely, high-throughput slices, low-delay 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 nonscalar form by considering the distinct channel features and service advantages of the terrestrial, aerial, and satellite components of acrshortpl SAGIN. A joint central and distributed multiagent 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 interslice subchannel and power sharing by relying on a centralized unit. Then, it optimizes the intraslice subchannel 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 Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.

https://eprints.bournemouth.ac.uk/39007/

Source: Scopus