Semi-Supervised Learning Approach for Efficient Resource Allocation with Network Slicing in O-RAN

• URL: http://arxiv.org/abs/2401.08861v2
• Date: Tue, 24 Sep 2024 19:37:20 GMT
• Title: Semi-Supervised Learning Approach for Efficient Resource Allocation with Network Slicing in O-RAN
• Authors: Salar Nouri, Mojdeh Karbalaee Motalleb, Vahid Shah-Mansouri, Seyed Pooya Shariatpanahi,
• Abstract summary: This paper introduces an innovative approach to the resource allocation problem. It aims to coordinate multiple independent x-applications (xAPPs) for network slicing and resource allocation in the Open Radio Access Network (O-RAN)
• Score: 5.1435595246496595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces an innovative approach to the resource allocation problem, aiming to coordinate multiple independent x-applications (xAPPs) for network slicing and resource allocation in the Open Radio Access Network (O-RAN). Our approach maximizes the weighted throughput among user equipment (UE) and allocates physical resource blocks (PRBs). We prioritize two service types: enhanced Mobile Broadband and Ultra-Reliable Low-Latency Communication. Two xAPPs have been designed to achieve this: a power control xAPP for each UE and a PRB allocation xAPP. The method consists of a two-part training phase. The first part uses supervised learning with a Variational Autoencoder trained to regress the power transmission, UE association, and PRB allocation decisions, and the second part uses unsupervised learning with a contrastive loss approach to improve the generalization and robustness of the model. We evaluate the performance by comparing its results to those obtained from an exhaustive search and deep Q-network algorithms and reporting performance metrics for the regression task. The results demonstrate the superior efficiency of this approach in different scenarios among the service types, reaffirming its status as a more efficient and effective solution for network slicing problems compared to state-of-the-art methods. This innovative approach not only sets our research apart but also paves the way for exciting future advancements in resource allocation in O-RAN.

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