Intelligent Hierarchical NOMA-Based Network Slicing in Cell-Free RAN for 6G Systems

In order to cope with the demand of explosively increasing service diversity and quality, network slicing has become the key technology of next-generation mobile communication. Mobile edge computing (MEC) can provide multi-dimensional resources and network functions at the edge of the network and re...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2024-05, Vol.23 (5), p.4724-4737
Main Authors: Ye, Feng, Li, Jiamin, Zhu, Pengcheng, Wang, Dongming, You, Xiaohu
Format: Article
Language:English
Subjects:
6G mobile communication
Algorithms
cell-free radio access network
Computational modeling
Computer architecture
deep reinforcement learning
Edge computing
Efficiency
Intelligent networks
Machine learning
MEC
Mobile computing
Multiagent systems
Network latency
Network slicing
NOMA
Nonorthogonal multiple access
Quality of service
Resource allocation
Resource management
Task analysis
Wireless networks
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Summary:In order to cope with the demand of explosively increasing service diversity and quality, network slicing has become the key technology of next-generation mobile communication. Mobile edge computing (MEC) can provide multi-dimensional resources and network functions at the edge of the network and reduce the delay of wireless networks. At the same time, non-orthogonal multiple access (NOMA) allows traffic to share resources and improve the spectral efficiency and energy efficiency of wireless networks. In this paper, we propose a hierarchical NOMA-based network slicing architecture in the 6G novel full-spectrum scalable cell-free radio access network with MECs and conduct joint allocation of communication, computing and caching resources at different resource granularity to meet the requirements of latency-critical applications with different latency. In order to realize the hierarchical joint resource allocation to improve system efficiency, we propose to address the optimal computing resource allocation and cache placement problem firstly through conventional optimization methods to reduce the action space and then use the multi-agent deep reinforcement learning algorithm for solving other complex coupling strategies. Simulation results further verify the effectiveness of the proposed intelligent network slicing scheme.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2023.3321717