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

By Shree Krishna Sharma, Xianbin Wang. IEEE Communications Surveys and Tutorials, 2018

356 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5ba7944dd68b0e18efad4ca11fb1475cdb0ff618

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Citations in the corpus

Enabling Massive IoT Toward 6G: A Comprehensive Survey (2021, methodology): “…the authors discussed the visions and enabling technologies of 6G thoroughly, which provided an insightful picture of 6G. Focusing on breakthrough technologies for IoT, the authors of [16] discussed the current issues in ultra-dense cellular IoT networks and machine learning-assisted solutions.”
Satellite Communications in the New Space Era: A Survey and Future Challenges (2020, background): “Machine Learning (ML) techniques in the literature can be broadly categorized into supervised, unsupervised and Reinforcement Learning (RL) [334].”
A Review of Machine Learning Approaches to Power System Security and Stability (2020, methodology): “Based on the deployed learning mechanisms, Sharma and Wang [46] categorized existing MLTs and deep learning techniques into supervised learning, unsupervised learning and reinforcement learning.”