Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods

• URL: http://arxiv.org/abs/2401.11731v1
• Date: Mon, 22 Jan 2024 07:19:16 GMT
• Title: Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
• Authors: Tianlun Hu, Qi Liao, Qiang Liu, Antonio Massaro, Georg Carle
• Abstract summary: Network slicing is a key technique in 5G and beyond for efficiently supporting diverse services. Deep learning models suffer limited generalization and adaptability to dynamic slicing configurations. We propose a novel framework that integrates constrained optimization methods and deep learning models.
• Score: 8.72339110741777
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network slicing is a key technique in 5G and beyond for efficiently supporting diverse services. Many network slicing solutions rely on deep learning to manage complex and high-dimensional resource allocation problems. However, deep learning models suffer limited generalization and adaptability to dynamic slicing configurations. In this paper, we propose a novel framework that integrates constrained optimization methods and deep learning models, resulting in strong generalization and superior approximation capability. Based on the proposed framework, we design a new neural-assisted algorithm to allocate radio resources to slices to maximize the network utility under inter-slice resource constraints. The algorithm exhibits high scalability, accommodating varying numbers of slices and slice configurations with ease. We implement the proposed solution in a system-level network simulator and evaluate its performance extensively by comparing it to state-of-the-art solutions including deep reinforcement learning approaches. The numerical results show that our solution obtains near-optimal quality-of-service satisfaction and promising generalization performance under different network slicing scenarios.

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