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

arXiv: https://arxiv.org/abs/2401.11731 · HTML: https://mlsanity.com/paper/2401.11731/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing](https://mlsanity.com/paper/2008.07614/) (2020)
- [RAN Resource Slicing in 5G Using Multi-Agent Correlated Q-Learning](https://mlsanity.com/paper/2107.01018/) (2021)
- [Constraint-Aware Deep Reinforcement Learning for End-to-End Resource Orchestration in Mobile Networks](https://mlsanity.com/paper/2110.04320/) (2021)
- [Sl-EDGE: Network Slicing at the Edge](https://mlsanity.com/paper/2005.00886/) (2020)
- [Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2202.12833/) (2022)

## Built on by (most cited first, 1 of 1)

- [Compositional Learning for Modular Multi-Agent Self-Organizing Networks](https://mlsanity.com/paper/2506.02616/) (2025)

## Abstract (arXiv)

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.