AI原生的开放无线接入网：从xApps和rApps到自主网络代理的路线图

中文摘要 AI 辅助

开放无线接入网（O-RAN）通过引入开放性、虚拟化、解耦和可编程智能（通过RAN智能控制器（RIC）实现），已成为未来无线系统的变革性范式。标准化接口和近实时控制环的可用性为将人工智能（AI）集成到无线接入网络管理和优化中创造了前所未有的机会。在过去几年中，人们提出了广泛的AI技术来解决O-RAN的关键挑战，如无线资源管理、网络切片、流量预测、移动性管理、干扰缓解和频谱共享。尽管取得了显著进展，现有解决方案通常仍是任务特定的，需要大量重新训练，并且在跨部署环境和网络条件的泛化能力上有限。本文对AI赋能的O-RAN系统进行了全面综述，并对无线网络中智能的演进提供了统一视角。我们首先考察O-RAN架构以及智能在近实时和非实时RIC框架中的作用。然后，我们开发了一个针对O-RAN的AI方法分类法，涵盖机器学习、深度强化学习（DRL）、数字孪生辅助优化以及新兴的基于基础模型的架构。

英文摘要

Open Radio Access Networks (O-RAN) have emerged as a transformative paradigm for future wireless systems by introducing openness, virtualization, disaggregation, and programmable intelligence through the RAN Intelligent Controller (RIC). The availability of standardized interfaces and near-real-time control loops has created unprecedented opportunities for integrating artificial intelligence (AI) into radio access network management and optimization. Over the past several years, a broad range of AI techniques have been proposed to address key O-RAN challenges such as radio resource management, network slicing, traffic prediction, mobility management, interference mitigation, and spectrum sharing. Despite significant progress, existing solutions often remain task-specific, require extensive retraining, and exhibit limited generalization across deployment environments and network conditions. This paper presents a comprehensive review of AI-enabled O-RAN systems and provides a unifying perspective on the evolution of intelligence in wireless networks. We first examine the O-RAN architecture and the role of intelligence within near-real-time and non-real-time RIC frameworks. We then develop a taxonomy of AI approaches for O-RAN, covering machine learning, deep reinforcement learning (DRL), digital-twin-assisted optimization, and emerging foundation-model-based architectures.

发表机构

• Holcombe Department of Electrical and Computer Engineering, Clemson University（克莱姆森大学霍爾莫普电气与计算机工程系）

机构由 AI 辅助整理，请以论文原文为准。