Low-Latency Communications for Community Resilience Microgrids: A Reinforcement Learning Approach
Low-Latency Communications for Community Resilience Microgrids: A Reinforcement Learning Approach
复制标题
DOI：
10.1109/tsg.2019.2931753
复制
发表时间：
2020-03-01
影响因子：
9.6
通讯作者：
Li, Jie
中科院分区：
文献类型：
--
作者：
Elsayed, Medhat;Erol-Kantarci, Melike;Li, Jie
机器学习和人工智能（AI）技术可以在无线网络的资源分配和调度程序设计中发挥关键作用，这些无线网络的目标应用具有严格的QoS要求，例如社区弹性微电网（crm）的近实时控制。具体而言，为了实现多个crm的集成控制和通信，需要大量的微电网设备与传统的移动用户设备共存，而传统的移动用户设备通常是由具有许多小蜂窝基站（SBSs）的自组织、密集的无线网络服务。在这种情况下，消息的快速传播变得具有挑战性。这就要求设计有效的资源分配和用户调度，以实现延迟最小化。本文介绍了一种资源分配算法，即延迟最小化q -学习（DMQ）方案，该方案在每个传输时间间隔（TTI）上使用强化学习来学习宏蜂窝基站（eNB）和SBSs的有效资源分配。与传统的比例公平（PF）算法和基于优化的分布式迭代资源分配（DIRA）算法相比，我们的方案可以分别减少66%和33%的延迟。此外，DMQ在吞吐量方面优于DIRA和PF，同时实现了最高的公平性。
Machine learning and artificial intelligence (AI) techniques can play a key role in resource allocation and scheduler design in wireless networks that target applications with stringent QoS requirements, such as near real-time control of community resilience microgrids (CRMs). Specifically, for integrated control and communication of multiple CRMs, a large number of microgrid devices need to coexist with traditional mobile user equipments (UEs), which are usually served with self-organized and densified wireless networks with many small cell base stations (SBSs). In such cases, rapid propagation of messages becomes challenging. This calls for a design of efficient resource allocation and user scheduling for delay minimization. In this paper, we introduce a resource allocation algorithm, namely, delay minimization Q-learning (DMQ) scheme, which learns the efficient resource allocation for both the macro cell base stations (eNB) and the SBSs using reinforcement learning at each time-to-transmit interval (TTI). Comparison with the traditional proportional fairness (PF) algorithm and an optimization-based algorithm, namely distributed iterative resource allocation (DIRA) reveals that our scheme can achieve 66% and 33% less latency, respectively. Moreover, DMQ outperforms DIRA, and PF in terms of throughput while achieving the highest fairness.