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Session Management for URLLC in 5G Open Radio Access Network: A Machine Learning Approach

机译：5G开放式无线电接入网络的URLLC会话管理：机器学习方法

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Supporting ultra-reliable and low latency communication (URLLC) has been a mandatory function for the International Mobile Telecommunications 2020 (IMT-2020) systems and so as 3GPP New Radio (NR). Conventionally, methods for URLLC primarily focus on performance enhancement on the air interfaces, which ignore a fact that data transmissions through the core network (CN) and the backhaul data network (DN) may invoke considerable latency and such latency may not be addressed solely by a local base station (BS). In this case, before the event of unacceptable latency occur, a BS should not accept the request of a new session creation, so as not to violate the latency and reliability requirements of the existing serving sessions and the new session. For this purpose, the critical challenge lies in how to proactively detect/cognize that the latency/reliability requirement violation event is going to occur, which relies on an effective experience update and process. To tackle this challenge, we particularly note the feature of event prediction in machine learning (ML) methods through experience training, especially the capability of sequential decision making to interact with an unknown environment in reinforcement learning (RL). In this paper, an intelligent session management is therefore proposed. Based on the recent innovation of Open Radio Access Network (O-RAN) to sustain the proposed RL scheme for intelligent session management, an O-RAN based BS is able to effectively configure/admin the resources for each existing serving sessions and the new session. Our simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC in O-RAN, to justify the potential of our approach in the design for 3GPP NR.
机译：支持超可靠和低延迟通信（URLLC）是国际移动电信2020（IMT-2020）系统的强制性功能，以及3GPP新型无线电（NR）。传统上，URLLC的方法主要关注空中界面上的性能增强，这忽略了通过核心网络（CN）和回程数据网络（DN）的数据传输可以调用可显着的延迟，并且可能无法仅通过本地基站（BS）。在这种情况下，在发生不可接受的延迟发生之前，BS不应接受新会话创建的请求，以免违反现有服务会话和新会话的延迟和可靠性要求。为此目的，临界挑战在于如何主动检测/认识到将发生延迟/可靠性要求违规事件，这依赖于有效的体验更新和流程。为了解决这一挑战，我们特别注意通过经验培训，特别是通过经验培训，特别是在加固学习中与未知环境相互作用的顺序决策（RL）中的序列决策能力的能力预测的特征。本文提出了智能会话管理。基于最近开放式无线电接入网络的创新（O-RAN）维持所提出的智能会话管理RL方案，基于O-RAN的BS能够有效地配置/管理每个现有服务会话和新会话的资源。我们的仿真结果充分展示了所提出的方法在支持U-RAN的方法中的实用性，以证明我们在3GPP NR设计中的方法的潜力。

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