The ever-increasing number of resource-constrained machine-type communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as enhanced mobile broadband (eMBB), massive machine type communications (mMTCs), and ultra-reliable and low latency communications (URLLCs), the mMTC brings the unique technical challenge of supporting a huge number of MTC devices in cellular networks, which is the main focus of this paper. The related challenges include quality of service (QoS) provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead, and radio access network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy random access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and narrowband IoT (NB-IoT). Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions toward addressing RAN congestion problem, and then identify potential advantages, challenges, and use cases for the applications of emerging machine learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity $Q$ -learning approach in the mMTC scenario along with the recent advances toward enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions.

中文翻译：

---

在超密集蜂窝物联网网络中实现大规模机器类型通信：当前问题和机器学习辅助解决方案

资源受限的机器类型通信 (MTC) 设备数量不断增加，这导致在动态和超密集无线环境中满足各种通信需求的关键挑战。在即将到来的 5G 及以后的蜂窝网络预计支持的不同应用场景中，例如增强型移动宽带 (eMBB)、海量机器类型通信 (mMTC) 和超可靠低延迟通信 (URLLC)，mMTC 带来了在蜂窝网络中支持大量 MTC 设备的独特技术挑战，这是本文的主要重点。相关挑战包括服务质量 (QoS) 配置、处理高度动态和零星的 MTC 流量、巨大的信令开销和无线接入网络 (RAN) 拥塞。在这方面，本文旨在识别和分析所涉及的技术问题，回顾最近的进展，突出潜在的解决方案并提出新的研究方向。首先，从 mMTC 功能和 QoS 配置问题的概述开始，我们介绍了蜂窝网络中 mMTC 的关键推动因素。除了 mMTC 场景中传统随机接入 (RA) 程序效率低下的亮点外，我们还介绍了新兴蜂窝物联网标准中的关键特性和信道接入机制，即 LTE-M 和窄带物联网 (NB-IoT) ）。随后，我们提出了一个具有 QoS 支持的传输调度性能分析框架以及短数据包传输中涉及的问题。下一个，我们详细概述了解决 RAN 拥塞问题的现有和新兴解决方案，然后确定新兴机器学习 (ML) 技术在超密集蜂窝网络中的应用的潜在优势、挑战和用例。在几种 ML 技术中，我们专注于低复杂度 $Q$ 学习方法在 mMTC 场景中的应用以及最近在提高其学习性能和收敛方面的进展。最后，我们讨论了一些开放的研究挑战和有前途的未来研究方向。我们专注于低复杂度 $Q$ 学习方法在 mMTC 场景中的应用以及最近在提高其学习性能和收敛方面的进展。最后，我们讨论了一些开放的研究挑战和有前途的未来研究方向。我们专注于低复杂度 $Q$ 学习方法在 mMTC 场景中的应用以及最近在提高其学习性能和收敛方面的进展。最后，我们讨论了一些开放的研究挑战和有前途的未来研究方向。