In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%.

中文翻译：

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5G 及以后 eMBB 和 URLLC 共存的智能资源切片：一种基于深度强化学习的方法

在本文中，我们研究了两种不同 5G 服务（即超可靠低延迟通信 (URLLC) 和增强型移动宽带 (eMBB)）的动态复用场景中的资源切片问题。eMBB 服务专注于高数据速率，而 URLLC 在延迟和可靠性方面非常严格。有鉴于此，将资源切片问题表述为一个优化问题，其目标是在 URLLC 可靠性约束下最大化 eMBB 数据速率，同时考虑 eMBB 数据速率的方差以减少立即调度的 URLLC 流量对eMBB 可靠性。为了解决公式化的问题，提出了一种基于优化辅助深度强化学习（DRL）的框架，包括：1）eMBB 资源分配阶段，以及 2）URLLC 调度阶段。在第一阶段，将优化问题分解为三个子问题，然后将每个子问题转化为凸形式，得到近似的资源分配解。在第二阶段，提出了一种基于 DRL 的算法来智能地在 eMBB 用户之间分配传入的 URLLC 流量。仿真结果表明，我们提出的方法可以满足严格的 URLLC 可靠性，同时保持 eMBB 可靠性高于 90%。