학술논문
Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
Document Type
Conference
Author
Source
GLOBECOM 2023 - 2023 IEEE Global Communications Conference Global Communications Conference, GLOBECOM 2023 - 2023 IEEE. :6346-6351 Dec, 2023
Subject
Language
ISSN
2576-6813
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 frame-work 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.