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.

翻译：开放无线接入网（O-RAN）技术已成为网络运营商的前瞻性解决方案，为其提供开放友好的运行环境。确保x应用（xAPP）的有效协调对于提升O-RAN的灵活性及优化网络性能至关重要。本文针对资源分配问题提出创新方法，旨在协调多个独立xAPP以实现O-RAN中的网络切片与资源分配。所提方法聚焦于最大化用户设备（UE）间的加权吞吐量，并完成物理资源块（PRB）的分配。我们优先处理两种业务类型：增强型移动宽带与超可靠低时延通信。为此，设计了两类xAPP：面向每个UE的功率控制xAPP与PRB分配xAPP。该方法包含两阶段训练流程：第一阶段采用监督学习，通过变分自编码器回归发射功率、用户关联及PRB分配决策；第二阶段采用对比损失函数的无监督学习，提升模型的泛化能力与鲁棒性。通过将所提方法的结果与穷举搜索算法、深度Q网络算法进行对比，并报告回归任务的性能指标，我们评估了该方法的有效性。此外，我们还评估了所提模型在不同业务场景下的性能表现。结果表明，与现有最先进方法相比，所提方法为网络切片问题提供了更高效的解决方案。