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

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
(mMTC) and Ultra-Reliable and Low Latency Communications
(URLLC), 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 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 scenario along with the recent advances towards
enhancing its learning performance and convergence. Finally,
we discuss some open research challenges and promising future
research directions.