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Enhanced K-means clustering with NOMA, SWIPT and HARQ, for massive M2M communication

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posted on 28.03.2022, 18:19 authored by Emerson Cabrera
One of the operating modes of the 5G cellular network, termed Massive Machine-to-Machine Communication (MMC), is currently limited by the scarcity of network resources in Long Term Evolution-Advanced (LTE-A) and the shortage of device battery power. MMC is expected to be utilised by applications such as sensor nodes, where attempted simultaneous access of network resources through the Random Access CHannel (RACH) in LTE-A, has been shown to lead to an overload and access problem. Therefore, this thesis proposes an enhanced K-means Clustering algorithim accompanied by Non-Orthogonal Multiple Access (NOMA), to enable the MMC operating mode. 'User Pairing' is applied to each cluster, with the strongest channel device assigned as the cluster head (CH), to enhance the network sum throughput. Energy Harvesting (EH) through Simultaneous Wireless Power and Information Transfer (SWIPT) is incorporated to address the DL Successive Interference Cancellation (SIC) and UL transmission power consumption concerns, and to increase the energy-efficiency (EE). A performance analysis was conducted, where our proposed scheme was shown: (a) to have a higher network sum throughput than the traditional K-means with a minimum rate requirement of 100-2000 kbps; and (b) that SIC and UL data transmission with SWIPT is feasible, with a minimum rate requirement of 100-1700 kbps. Another operating mode of 5G, termed Ultra-Reliable Communication (URC), is expected to be utilised by applications such as mission critical industrial control, medical and Vehicle-to-Everything (V2X). These applications will be under the constraints of high availability, ultra-reliability and ultra-low latency. Hybrid Automatic Repeat reQuest (HARQ) has been proven to improve the reliability of data transmission by the retransmission (RTX) of erroneous packets during poor channel conditions. This thesis also proposes an enhanced NOMA HARQ scheme to accompany the enhanced K-means clustering algorithim, to improve the RTX process and therefore reduce the delay incurred from the possibility of multiple RTX. A performance analysis was conducted, by comparing our enhanced NOMA HARQ scheme with the competing Chase Combining (CC) HARQ and the LTE-A HARQ OMA scheme, in terms of outage probability, reliability, and delay. Our enhanced NOMA HARQ scheme was shown to have a lower outage probability and higher achieveable rate compared to CC-HARQ and LTE-A HARQ OMA scheme. This was due to the incorporation of Incremental Redundancy (IR) HARQ and cooperative relaying NOMA. A lower outage probability led to increased reliability and therefore decreased delay compared to the CC-HARQ and LTE-HARQ OMA schemes. Naturally, these two operating modes of 5G are closely linked together because 5G is regarded as an important enabler for the Internet of Things (IoT). The IoT consists of Massive Machine-Type Communication (mMTC) and Ultra-Reliable Low-Latency Communication (URLLC), which are the massive form of Machine-to-Machine (M2M) Communication and URC with low-latency communication, respectively. Therefore M2M and URC were both addressed in this thesis, by extending the proposed enhanced K-means Clustering algortihim NOMA scheme with SWIPT, to include an enhanced HARQ NOMA ccheme -- abstract.

History

Table of Contents

Chapter 1. Introduction -- Chapter 2. Literature review -- Chapter 3. Downlink NOMA with SWIPT -- Chapter 4. Uplink NOMA with SWIPT -- Chapter 5. Enhanced NOMA HARQ scheme -- Chapter 6. Thesis conclusions and future work

Notes

Theoretical thesis. Bibliography: pages 123-135

Awarding Institution

Macquarie University

Degree Type

Thesis PhD

Degree

PhD, Macquarie University, Faculty of Science and Engineering, School of Engineering

Department, Centre or School

School of Engineering

Year of Award

2020

Principal Supervisor

Rein Vesilo

Rights

Copyright Emerson Cabrera 2020 Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (xx, 135 pages)

Former Identifiers

mq:72244 http://hdl.handle.net/1959.14/1282848