Open-Radio Access Network (O-RAN) has become an important paradigm for 5G and beyond radio access networks. This paper presents an xApp called xSlice for the Near-Real-Time (Near-RT) RAN Intelligent Controller (RIC) of 5G O-RANs. xSlice is an online learning algorithm that adaptively adjusts MAC-layer resource allocation in response to dynamic network states, including time-varying wireless channel conditions, user mobility, traffic fluctuations, and changes in user demand. To address these network dynamics, we first formulate the Quality-of-Service (QoS) optimization problem as a regret minimization problem by quantifying the QoS demands of all traffic sessions through weighting their throughput, latency, and reliability. We then develop a deep reinforcement learning (DRL) framework that utilizes an actor-critic model to combine the advantages of both value-based and policy-based updating methods. A graph convolutional network (GCN) is incorporated as a component of the DRL framework for graph embedding of RAN data, enabling xSlice to handle a dynamic number of traffic sessions. We have implemented xSlice on an O-RAN testbed with 10 smartphones and conducted extensive experiments to evaluate its performance in realistic scenarios. Experimental results show that xSlice can reduce performance regret by 67% compared to the state-of-the-art solutions. Source code is available at https://github.com/xslice-5G/code.

翻译：开放无线接入网络（O-RAN）已成为5G及未来无线接入网络的重要范式。本文为5G O-RAN的近实时无线接入网络智能控制器（RIC）提出了一种名为xSlice的xApp。xSlice是一种在线学习算法，能够根据动态网络状态自适应地调整MAC层资源分配，这些状态包括时变的无线信道条件、用户移动性、流量波动以及用户需求的变化。为应对这些网络动态，我们首先通过加权所有业务会话的吞吐量、时延和可靠性来量化其服务质量需求，从而将服务质量优化问题建模为一个遗憾最小化问题。随后，我们开发了一个深度强化学习框架，该框架利用演员-评论家模型，结合了基于值和基于策略的更新方法的优势。我们将图卷积网络作为该深度强化学习框架的一个组成部分，用于对无线接入网络数据进行图嵌入，使得xSlice能够处理动态数量的业务会话。我们已在配备10部智能手机的O-RAN测试平台上实现了xSlice，并进行了大量实验以评估其在真实场景下的性能。实验结果表明，与现有最先进的解决方案相比，xSlice能够将性能遗憾降低67%。源代码可在 https://github.com/xslice-5G/code 获取。