Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
This work addresses the problem of dynamic resource allocation in network slicing for 5G and beyond, offering a scalable solution that improves upon existing methods, though it is incremental in nature.
The paper tackles the limited generalization and adaptability of deep learning models in network slicing for 5G by integrating constrained optimization with deep learning, resulting in near-optimal quality-of-service satisfaction and promising generalization performance in various scenarios.
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.