Semi-Supervised Learning Approach for Efficient Resource Allocation with
  Network Slicing in O-RAN
Salar Nouri Mojdeh Karbalaee Motalleb Vahid Shah-Mansouri Seyed Pooya Shariatpanahi
The Open Radio Access Network (O-RAN) technology has emerged as a promising
solution for network operators, providing them with an open and favorable
environment. Ensuring effective coordination of x-applications (xAPPs) is
crucial to enhance flexibility and optimize network performance within the
O-RAN. In this paper, we introduce an innovative approach to the resource
allocation problem, aiming to coordinate multiple independent xAPPs for network
slicing and resource allocation in O-RAN. Our proposed method focuses on
maximizing the weighted throughput among user equipments (UE), as well as
allocating physical resource blocks (PRBs). We prioritize two service types,
namely enhanced Mobile Broadband and Ultra Reliable Low Latency Communication.
To achieve this, we have designed two xAPPs: a power control xAPP for each UE
and a PRB allocation xAPP. The proposed method consists of a two-part training
phase, where the first part uses supervised learning with a Variational
Autoencoder trained to regress the power transmission as well as the user
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 of our proposed method by
comparing its results to those obtained from an exhaustive search algorithm,
deep Q-network algorithm, and by reporting performance metrics for the
regression task. We also evaluate the proposed model's performance in different
scenarios among the service types. The results show that the proposed method is
a more efficient and effective solution for network slicing problems compared
to state-of-the-art methods.