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.

翻译：本文针对开放无线接入网络（O-RAN）中的资源分配问题，提出一种创新方法，旨在协调多个独立的x应用程序（xAPP）以实现网络切片与资源分配。我们的方法通过优化用户设备（UE）的加权吞吐量并分配物理资源块（PRB），重点保障增强型移动宽带和超可靠低时延通信两类服务。为此我们设计了两种xAPP：面向各UE的功率控制xAPP与PRB分配xAPP。该方法包含两个阶段的训练过程：第一阶段采用监督学习，利用变分自编码器对功率传输、UE关联及PRB分配决策进行回归训练；第二阶段采用无监督学习，通过对比损失方法提升模型的泛化能力与鲁棒性。我们通过将本方法与穷举搜索及深度Q网络算法的结果进行对比，并汇报回归任务的性能指标，从而评估其性能。实验结果表明，该方法在不同服务类型的多种场景中均展现出卓越的效能，证实其相较于现有前沿方法能为网络切片问题提供更高效、更优质的解决方案。这一创新方法不仅凸显了本研究的独特性，更为O-RAN资源分配领域的未来发展开辟了新的道路。