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 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.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Low-power Physical-layer Design for LTE Based Very NarrowBand IoT (VNB - IoT) Communication

abstract: With the new age Internet of Things (IoT) revolution, there is a need to connect a wide range of devices with varying throughput and performance requirements. In this thesis, a wireless system is proposed which is targeted towards very low power, delay insensitive IoT applications with low throughput requirements. The low cost receivers for such devices will have very low complexity, consume very less power and hence will run for several years. Long Term Evolution (LTE) is a standard developed and administered by 3rd Generation Partnership Project (3GPP) for high speed wireless communications for mobile devices. As a part of Release 13, another standard called narrowband IoT (NB-IoT) was introduced by 3GPP to serve the needs of IoT applications with low throughput requirements. Working along similar lines, this thesis proposes yet another LTE based solution called very narrowband IoT (VNB-IoT), which further reduces the complexity and power consumption of the user equipment (UE) while maintaining the base station (BS) architecture as defined in NB-IoT. In the downlink operation, the transmitter of the proposed system uses the NB-IoT resource block with each subcarrier modulated with data symbols intended for a different user. On the receiver side, each UE locks to a particular subcarrier frequency instead of the entire resource block and operates as a single carrier receiver. On the uplink, the system uses a single-tone transmission as specified in the NB-IoT standard. Performance of the proposed system is analyzed in an additive white Gaussian noise (AWGN) channel followed by an analysis of the inter carrier interference (ICI). Relationship between the overall filter bandwidth and ICI is established towards the end.Dissertation/ThesisMasters Thesis Electrical Engineering 201