Massive machine-type communications (mMTC) is one of the main three focus
areas in the 5th generation (5G) of mobile standards to enable connectivity of
a massive number of internet of things (IoT) devices with little or no human
intervention. In conventional human-type communications (HTC), due to the
limited number of available radio resources and orthogonal/non-overlapping
nature of existing resource allocation techniques, users need to compete for
connectivity through a random access (RA) process, which may turn into a
performance bottleneck in mMTC. In this context, non-orthogonal multiple access
(NOMA) has emerged as a potential technology that allows overlapping of
multiple users over a radio resource, thereby creating an opportunity to enable
more autonomous and grant-free communication, where devices can transmit data
whenever they need. The existing literature on NOMA schemes majorly considers
centralized scheduling based HTC, where users are already connected, and
various system parameters like spreading sequences, interleaving patterns,
power control, etc., are predefined. Contrary to HTC, mMTC traffic is different
with mostly uplink communication, small data size per device, diverse quality
of service, autonomous nature, and massive number of devices. Hence, the
signaling overhead and latency of centralized scheduling becomes a potential
performance bottleneck. To tackle this, grant-free access is needed, where mMTC
devices can autonomously transmit their data over randomly chosen radio
resources. This article, in contrast to existing surveys, comprehensively
discusses the recent advances in NOMA from a grant-free connectivity
perspective. Moreover, related practical challenges and future directions are
discussed.

本文全面讨论了来自免授权连接角度的非正交多址接入 (NOMA) 的最新进展，探讨了相关的实际挑战和未来方向。

物联网无授权非正交多用户接入：一项调研

Grant-free Non-orthogonal Multiple Access for IoT: A Survey

Massive machine-type communication (mMTC) is a newly introduced service
category in 5G wireless communication systems to support a variety of
Internet-of-Things (IoT) applications. In recovering sparsely represented
multi-user vectors, compressed sensing based multi-user detection (CS-MUD) can
be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal
multiple access (NOMA) environments. In CS-MUD, active user detection (AUD) and
channel estimation (CE) should be performed before data detection. In this
paper, we propose the expectation propagation based joint AUD and CE
(EP-AUD/CE) technique for mMTC networks. The expectation propagation (EP)
algorithm is a Bayesian framework that approximates a computationally
intractable probability distribution to an easily tractable distribution. The
proposed technique finds the best approximation of the posterior distribution
of the sparse channel vector. Using the approximate distribution, AUD and CE
are jointly performed. We show by numerical simulations that the proposed
technique substantially enhances AUD and CE performances over competing
algorithms.

本文介绍了一种基于期望传播算法的联合主动用户检测和信道估计技术，通过该技术，可为 5G 通信系统中的 mMTC 网络提供更好的表现，特别是在压缩感知和非正交多址接入 (NOMA) 环境下。

基于 EP 算法的大规模机器型通讯联合激活用户检测和信道估计

EP-based Joint Active User Detection and Channel Estimation for Massive  Machine-Type Communications

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 eMBB, mMTC and URLLC, mMTC brings the
unique technical challenge of supporting a huge number of MTC devices, which is
the main focus of this paper. The related challenges include 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 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 towards 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 scenarios. Finally, we discuss
some open research challenges and promising future research directions.

本文针对 mMTC 场景中海量 MTC 设备的 QoS 保障、RAN 拥塞等问题展开研究，综述了现有解决方案并提出了新的解决方向，其中包括利用机器学习技术解决 mMTC 中的 QoS 问题。

超密集蜂窝物联网中的大规模机器型通信：现有问题和机器学习辅助解决方案

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT  Networks: Current Issues and Machine Learning-Assisted Solutions

Machine-type communications (MTC) are expected to play an essential role
within future 5G systems. In the FP7 project METIS, MTC has been further
classified into "massive Machine-Type Communication" (mMTC) and "ultra-reliable
Machine-Type Communication" (uMTC). While mMTC is about wireless connectivity
to tens of billions of machine-type terminals, uMTC is about availability, low
latency, and high reliability. The main challenge in mMTC is scalable and
efficient connectivity for a massive number of devices sending very short
packets, which is not done adequately in cellular systems designed for
human-type communications. Furthermore, mMTC solutions need to enable wide area
coverage and deep indoor penetration while having low cost and being energy
efficient. In this article, we introduce the physical (PHY) and medium access
control (MAC) layer solutions developed within METIS to address this challenge.

介绍了 METIS 项目中为了解决物联网设备大规模联网和无线覆盖等问题开发的物理和媒介访问控制层解决方案。