# Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning

arXiv: https://arxiv.org/abs/2401.08861 · HTML: https://mlsanity.com/paper/2401.08861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [A Theoretical Analysis of Deep Q-Learning](https://mlsanity.com/paper/1901.00137/) (2019)
- [On Loss Functions for Deep Neural Networks in Classification](https://mlsanity.com/paper/1702.05659/) (2017)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions](https://mlsanity.com/paper/2308.06250/) (2023)
- [5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View](https://mlsanity.com/paper/1804.05057/) (2018)
- [Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!](https://mlsanity.com/paper/2111.13754/) (2021)
- [Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning](https://mlsanity.com/paper/2012.01991/) (2020)
- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [Self-play Learning Strategies for Resource Assignment in Open-RAN Networks](https://mlsanity.com/paper/2103.02649/) (2021)
- [An Overview of the 3GPP Study on Artificial Intelligence for 5G New Radio](https://mlsanity.com/paper/2308.05315/) (2023)
- [Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks](https://mlsanity.com/paper/2401.07643/) (2024)
- [Contrastive Self-Supervised Learning for Wireless Power Control](https://mlsanity.com/paper/2010.11909/) (2020)
- [Federated Learning Assisted Deep Q-Learning for Joint Task Offloading and Fronthaul Segment Routing in Open RAN](https://mlsanity.com/paper/2308.07258/) (2023)
- [Joint Power Allocation and Network Slicing in an Open RAN System](https://mlsanity.com/paper/1911.01904/) (2019)
- [Artificial Intelligence for 6G Networks: Technology Advancement and Standardization](https://mlsanity.com/paper/2204.00914/) (2022)
- [Moving Target Defense based Secured Network Slicing System in the O-RAN Architecture](https://mlsanity.com/paper/2309.13444/) (2023)
- [An Optimal Application-Aware Resource Block Scheduling in LTE](https://mlsanity.com/paper/1405.7446/) (2014)

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

- [Towards Secure Intelligent O-RAN Architecture: Vulnerabilities, Threats and Promising Technical Solutions using LLMs](https://mlsanity.com/paper/2411.08640/) (2024)

## Abstract (arXiv)

This paper introduces a novel generative AI (GAI)-driven, unified semi-supervised learning architecture for optimizing resource allocation and network slicing in O-RAN. Termed Generative Semi-Supervised VAE-Contrastive Learning, our approach maximizes the weighted user equipment (UE) throughput and allocates physical resource blocks (PRBs) to enhance the quality of service for eMBB and URLLC services. The GAI framework utilizes a dedicated xApp for intelligent power control and PRB allocation. This integrated GAI model synergistically combines the generative power of a VAE with contrastive learning to achieve robustness in an end-to-end trainable system. It is a semi-supervised training approach that concurrently optimizes supervised regression of resource allocation decisions (i.e., power, UE association, PRB) and unsupervised contrastive objectives. This intrinsic fusion improves the precision of resource management and model generalization in dynamic mobile networks. We evaluated our GAI methodology against exhaustive search and deep Q-Network algorithms using key performance metrics. Results show our integrated GAI approach offers superior efficiency and effectiveness in various scenarios, presenting a compelling GAI-based solution for critical network slicing and resource management challenges in next-generation O-RAN systems.