Enabling Technologies for Internet of Things: Licensed and Unlicensed Techniques

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

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Graphene-based terahertz reconfigurable printed ridge gap waveguide structure

Graphene-based microwave devices have enabled reconfigurability, thus paving the way to the realization of flexible wireless terahertz systems with featured performances. Despite great progress in the development of graphene-based terahertz devices in the literature, high insertion loss and wide tunable range are still significant challenges at such high frequencies. In this work, we introduce the use of graphene to implement a reconfigurable printed ridge gap waveguide (RPRGW) structure over the terahertz frequency range for the first time. This guiding structure is suitable for both millimeter and terahertz wave applications due to its supporting quasi-TEM mode, which exhibits low dispersion compared to other traditional guiding structures. The presented solution is featured with low loss as the signal propagates in a lossless air gap, which is separated from the lossy graphene elements responsible for the reconfigurable behavior. In addition, this guiding structure is deployed to implement a tunable RPPGW power divider as an application example for the proposed structure