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arXiv 2103.01801eess.SPcs.LG

基于调度eMBB流量的深度强化学习用于URLLC数据管理

Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic

Fabio Saggese, Luca Pasqualini, Marco Moretti, Andrea Abrardo

备注：
This work has been submitted to the IEEE for possible publication
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AI总结：

本文提出深度强化学习算法，用于在调度eMBB流量的物理层资源上切分URLLC和eMBB流量，通过动态分配URLLC流量来满足其严格要求。

摘要：

随着5G和Beyond 5G (B5G)网络的研究，如何管理不同类型流量的共存成为新的研究问题。本文提出一种深度强化学习（DRL）算法，用于在超可靠低时延通信（URLLC）和增强移动宽带（eMBB）流量之间切分可用物理层资源。具体而言，在我们的设置中，时间-频率资源网格完全被eMBB流量占用，我们训练DRL代理使用近端策略优化（PPO）算法动态分配进入的URLLC流量，通过删除eMBB码字。假设每个eMBB码字可以容忍一定量的删除，超过该限制会导致失真，我们证明了DRL代理设计的策略不会违反URLLC流量的时延要求，同时在与其他最先进的方案相比，将处于失真状态的eMBB码字数量保持在最低水平。

英文摘要：

With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each with very stringent but completely different requirements. In this paper we propose a deep reinforcement learning (DRL) algorithm to slice the available physical layer resources between ultra-reliable low-latency communications (URLLC) and enhanced Mobile BroadBand (eMBB) traffic. Specifically, in our setting the time-frequency resource grid is fully occupied by eMBB traffic and we train the DRL agent to employ proximal policy optimization (PPO), a state-of-the-art DRL algorithm, to dynamically allocate the incoming URLLC traffic by puncturing eMBB codewords. Assuming that each eMBB codeword can tolerate a certain limited amount of puncturing beyond which is in outage, we show that the policy devised by the DRL agent never violates the latency requirement of URLLC traffic and, at the same time, manages to keep the number of eMBB codewords in outage at minimum levels, when compared to other state-of-the-art schemes.

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