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Enabling Technologies for Internet of Things: Licensed and Unlicensed Techniques

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Enabling Technologies for Internet of Things: Licensed and Unlicensed Techniques

Elsaadany, Mahmoud (2018) Enabling Technologies for Internet of Things: Licensed and Unlicensed Techniques. PhD thesis, Concordia University.

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Abstract

The Internet of Things (IoT) is a novel paradigm which is shaping the evolution of the future Internet. According to the vision underlying the IoT, the next step in increasing the ubiquity of the Internet, after connecting people anytime and everywhere, is to connect inanimate objects. By providing objects with embedded communication capabilities and a common addressing scheme, a highly distributed and ubiquitous network of seamlessly connected heterogeneous devices is formed, which can be fully integrated into the current
Internet and mobile networks, thus allowing for the development of new intelligent services available anytime, anywhere, by anyone and anything. Such a vision is also becoming known under the name of Machine-to-Machine (M2M), where the absence of human interaction in the system dynamics is further emphasized.
A massive number of wireless devices will have the ability to connect to the Internat through the IoT framework. With the accelerating pace of marketing such framework, the new wireless communications standards are studying/proposing solutions to incorporate the services needed for the IoT. However, with an estimate of 30 billion connected devices, a lot of challenges are facing the current wireless technology.
In our research, we address a variety of technology candidates for enabling such a massive framework. Mainly, we focus on the nderlay cognitive radio networks as the unlicensed candidate for IoT. On the other hand, we look into the current efforts done by the standardization bodies to accommodate the requirements of the IoT into the current cellular networks.
Specifically, we survey the new features and the new user equipment
categories added to the physical layer of the LTE-A.

In particular, we study the performance of a dual-hop cognitive radio network sharing the spectrum of a primary network in an underlay fashion. In particular, the cognitive network consists of a source, a destination, and multiple nodes employed as amplify-and-forward
relays. To improve the spectral efficiency, all relays are allowed to instantaneously transmit to the destination over the same frequency band. We present the optimal power allocation that maximizes the received signal-to-noise ratio (SNR) at the destination while satisfying the interference constrains of the primary network. The optimal power allocation is obtained through an eigen-solution of a channel-dependent matrix, and is shown to transform the transmission over the non-orthogonal relays into parallel channels. Furthermore, while the secondary destination is equipped with multiple antennas, we propose an antenna selection scheme to select the antenna with the highest SNR. To this end, we propose a clustering scheme to subgroup the available relays and use antenna selection at the receiver to extract the same diversity order. We show that random clustering causes the system to lose some of the available degrees of freedom. We provide analytical expression of the outage probability of the system for the random clustering and the proposed maximum-SNR clustering scheme with antenna selection. In addition, we adapt our design to increase the energy-efficiency of the overall network without significant loss in the data rate.
In the second part of this thesis, we will look into the current efforts done by the standardization bodies to accommodate the equirements of the IoT into the current cellular networks. Specifically, we present the new features and the new user equipment categories added to the physical layer of the LTE-A. We study some of the challenges facing the LTE-A when dealing with Machine Type communications (MTC). Specifically, the MTC Physical Downlink control channel (MPDCCH) is among the newly introduced features in the LTE-A that carries the downlink control information (DCI) for MTC devices. Correctly decoding the PDCCH, mainly depends on the channel estimation used to compensate for the channel errors during transmission, and the choice of such technique will affect both the complexity and the performance of the user equipment. We propose and assess the performance of a simple channel estimation technique depends in essence on the Least Squares (LS) estimates of the pilots signal and linear interpolations for low-Doppler channels associated with the
MTC application.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (PhD)
Authors:Elsaadany, Mahmoud
Institution:Concordia University
Degree Name:Ph. D.
Program:Electrical and Computer Engineering
Date:9 April 2018
Thesis Supervisor(s):Hamouda, Walaa
ID Code:983925
Deposited By: MAHMOUD ELSAADANY
Deposited On:31 Oct 2018 16:20
Last Modified:31 Oct 2018 16:20
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