[论文解读] Intelligent O-RAN for Beyond 5G and 6G Wireless Networks

Solmaz Niknam, Abhishek Roy+6 more|arXiv (Cornell University)|May 17, 2020

Advanced Wireless Communication Technologies被引用 68

一句话总结

本文展示了一种面向O-RAN的智能拥塞管理方案，利用基于LSTM的流量预测来触发小区分割，在真实孟买网络数据上进行评估。

ABSTRACT

Building on the principles of openness and intelligence, there has been a concerted global effort from the operators towards enhancing the radio access network (RAN) architecture. The objective is to build an operator-defined RAN architecture (and associated interfaces) on open hardware that provides intelligent radio control for beyond fifth generation (5G) as well as future sixth generation (6G) wireless networks. Specifically, the open-radio access network (O-RAN) alliance has been formed by merging xRAN forum and C-RAN alliance to formally define the requirements that would help achieve this objective. Owing to the importance of O-RAN in the current wireless landscape, this article provides an introduction to the concepts, principles, and requirements of the Open RAN as specified by the O-RAN alliance. In order to illustrate the role of intelligence in O-RAN, we propose an intelligent radio resource management scheme to handle traffic congestion and demonstrate its efficacy on a real-world dataset obtained from a large operator. A high-level architecture of this deployment scenario that is compliant with the O-RAN requirements is also discussed. The article concludes with key technical challenges and open problems for future research and development.

研究动机与目标

• 介绍Open RAN（O-RAN）的概念，以及开放性和智能在新一代网络中的作用。
• 提出在O-RAN内用于拥塞预测和缓解的基于ML的无线资源管理方案。
• 在真实的孟买LTE数据集上验证该方法，并将解决方案映射到O-RAN控制循环和接口。
• 讨论面向智能RAN的部署体系结构、实现步骤及实际挑战。

提出的方法

• 采用LSTM循环神经网络来学习和预测时序流量模式及潜在拥塞。
• 将拥塞定义为：用户感知的IP吞吐量平均值 < 1 Mbps 且 DL-PRB利用率 > 80%。
• 在真实孟买蜂窝数据集（17个eNB、每个18个小区、25天）上使用2层、12个LSTM单元训练模型。
• 在O-RAN框架内，将训练阶段部署在non-RT RIC，推理阶段部署在near-RT RIC。
• 基于预测拥塞，通过小区分割触发拥塞缓解，必要时结合双连接。
• 描述端到端部署流程，使ML训练/推理与O1/A1/E2接口和xAPP CPM对齐。

实验结果

研究问题

• RQ1 基于LSTM的流量预测是否能够准确预报密集城域RAN中的拥塞？
• RQ2 基于ML预测的先发性小区分割是否能提升用户感知吞吐量和DL-PRB利用率？
• RQ3 如何将基于ML的拥塞管理循环与O-RAN接口（O1、A1、E2）及xAPPs进行集成？
• RQ4 哪些部署体系结构和数据工作流能够支持面向Beyond-5G/6G场景的智能RAN？

主要发现

• LSTM模型在流量参数上的预测准确度平均为92.64%。
• 基于拥塞预测的先发性小区分割显著提升用户感知的IP吞吐量。
• DL-PRB利用率和吞吐量指标在定义的KPI目标内对ML驱动的拥塞缓解作出响应。
• 所提方法映射到O-RAN控制循环，ML训练在non-RT RIC，推理在near-RT RIC。
• 高层部署体系结构显示数据收集、ML训练/推理、CPM xAPP以及基于E2的行动部署。