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.

中文翻译：

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社区弹性微电网的低延迟通信：强化学习方法

机器学习和人工智能（AI）技术可以在针对具有严格QoS要求的应用程序的无线网络中的资源分配和调度程序设计中发挥关键作用，例如对社区弹性微电网（CRM）进行近乎实时的控制。具体而言，对于多个CRM的集成控制和通信，大量微网格设备需要与传统移动用户设备（UE）共存，而传统移动用户设备通常与带有许多小型基站（SBS）的自组织和密集型无线网络一起使用。在这种情况下，消息的快速传播变得充满挑战。这要求设计有效的资源分配和用户调度以最小化延迟。本文介绍一种资源分配算法，即延迟最小化Q学习（DMQ）方案，它利用每个时间到发射间隔（TTI）的强化学习来学习宏小区基站（eNB）和SBS的有效资源分配。与传统的比例公平（PF）算法和基于优化的算法（即分布式迭代资源分配（DIRA））相比，我们的方案可分别减少66％和33％的延迟。此外，DMQ在吞吐量方面胜过DIRA和PF，同时实现了最高的公平性。即分布式迭代资源分配（DIRA）表明，我们的方案可以分别减少66％和33％的延迟。此外，DMQ在吞吐量方面胜过DIRA和PF，同时实现了最高的公平性。即分布式迭代资源分配（DIRA）表明，我们的方案可以分别减少66％和33％的延迟。此外，DMQ在吞吐量方面胜过DIRA和PF，同时实现了最高的公平性。