In this dissertation, power allocation approaches considering path loss, shadowing, and Rayleigh and Nakagami-m fading are proposed. The goal is to improve power consumption, and energy and throughput efficiency based on user target signal to interference plus noise ratio (SINR) requirements and an outage probability threshold. First, using the moment generating function (MGF), the exact outage probability over Rayleigh and Nakagami-m fading channels is derived. Then upper and lower bounds on the outage probability are derived using the Weierstrass, Bernoulli and exponential inequalities. Second, the problem of minimizing the user power subject to outage probability and user target SINR constraints is considered. The corresponding power allocation problems are solved using Perron-Frobenius theory and geometric programming (GP). A GP problem can be transformed into a nonlinear convex optimization problem using variable substitution and then solved globally and efficiently by interior point methods. Then, power allocation problems for throughput maximization and energy efficiency are proposed. As these problems are in a convex fractional programming form, parametric transformation is used to convert the original problems into subtractive optimization problems which can be solved iteratively. Simulation results are presented which show that the proposed approaches are better than existing schemes in terms of power consumption, throughput, energy efficiency and outage probability. Prioritized cell association and power allocation (CAPA) to solve the load balancing issue in heterogeneous networks (HetNets) is also considered in this dissertation. A Hetnet is a group of macrocell base stations (MBSs) underlaid by a diverse set of small cell base stations (SBSs) such as microcells, picocells and femtocells. These networks are considered to be a good solution to enhance network capacity, improve network coverage, and reduce power consumption. However, HetNets are limited by the disparity of power levels in the different tiers. Conventional cell association approaches cause MBS overloading, SBS underutilization, excessive user interference and wasted resources. Satisfying priority user (PU) requirements while maximizing the number of normal users (NUs) has not been considered in existing power allocation algorithms. Two stage CAPA optimization is proposed to address the prioritized cell association and power allocation problem. The first stage is employed by PUs and NUs and the second stage is employed by BSs. First, the product of the channel access likelihood (CAL) and channel gain to interference plus noise ratio (GINR) is considered for PU cell association while network utility is considered for NU cell association. Here, CAL is defined as the reciprocal of the BS load. In CAL and GINR cell association, PUs are associated with the BSs that provide the maximum product of CAL and GINR. This implies that PUs connect to BSs with a low number of users and good channel conditions. NUs are connected to BSs so that the network utility is maximized, and this is achieved using an iterative algorithm. Second, prioritized power allocation is used to reduce power consumption and satisfy as many NUs with their target SINRs as possible while ensuring that PU requirements are satisfied. Performance results are presented which show that the proposed schemes provide fair and efficient solutions which reduce power consumption and have faster convergence than conventional CAPA schemes. / Graduate

In 5G systems, two radio air interfaces, evolved LTE and New Radio (NR), will coexist. By using millimeter waves, NR will provide high throughputs, but the higher frequencies will also lead to increased losses and a worse coverage. Multi-connectivity is therefore envisioned as a way to tackle these effects by connecting to multiple base stations simultaneously, allowing users to benefit from both air interfaces’ advantages. In this thesis, we investigate how multi-connectivity can be used efficiently in ultra-dense networks, a new paradigm in which the number of access nodes exceeds the number of users within the network. A framework for secondary cell association is presented and an energy efficiency’s condition is proposed. Upper and lower bounds of the network’s energy efficiency are analytically expressed. Algorithms for secondary cell selection are designed and evaluated through simulations. Multi-connectivity showed an improvement of up to 50% in reliability and and an increase of up to 20% in energy efficiency.

Depuis plusieurs années, les opérateurs de téléphonie mobile sont confrontés à une croissance considérable du trafic de données mobiles. Dans un tel contexte, la technologie Cloud Radio Access Network (CRAN) qui intègre les solutions de Cloud Computing aux réseaux d’accès radio est considérée comme une nouvelle architecture pour les futures générations de réseaux 5G. L’approche CRAN permet une optimisation globale des fonctions de traitement en bande de base du signal et de la gestion des ressources radio pour l’ensemble des RRH et des utilisateurs. Parallèlement, les réseaux hétérogènes (HetNets) ont été proposés pour augmenter efficacement la capacité et la couverture du réseau 5G tout en réduisant la consommation énergétique. En combinant les avantages du Cloud avec ceux des réseaux HetNets, le concept de réseaux H-CRAN (Heterogeneous Cloud Radio Access Networks) est né et est considéré comme l’une des architectures les plus prometteuses pour répondre aux exigences des futurs systèmes. Plus particulièrement, nous abordons le problème important de l’optimisation jointe de l’association utilisateur-RRH et de la solution de beamforming sur la liaison descendante d’un système H-CRAN. Nous formulons un problème de maximisation du débit total du système sous des contraintes de mobilité et d’imperfection de CSI (Channel State Information). Notre principal défi consiste à concevoir une solution capable de maximiser le débit tout en permettant, contrairement aux autres solutions de référence, de réduire la complexité de calcul, et les coûts de signalisation et de feedback CSI dans divers environnements. Notre étude commence par proposer un algorithme Hybride, qui active périodiquement des schémas de clustering dynamiques et statiques pour aboutir à un compromis satisfaisant entre optimalité et le coût en complexité et signalisation CSI et réassociation. L’originalité de l’algorithme Hybride réside aussi dans sa prise en compte de la dimension temporelle du processus d’allocation sur plusieurs trames successives plutôt que son optimalité (ou sous-optimalité) pour la seule trame d’ordonnancement courante. De plus, nous développons une analyse des coûts de l’algorithme en fonction de plusieurs critères afin de mieux appréhender le compromis entre les nombreux paramètres impliqués. La deuxième contribution de la thèse s’intéresse au problème sous la perspective de la mobilité utilisateur. Deux variantes améliorées de l’algorithme Hybride sont proposées : ABUC (Adaptive Beamforming et User Clustering), une version adaptée à la mobilité des utilisateurs et aux variations du canal radio, et MABUC (Mobility-Aware Beamforming et User Clustering), une version améliorée qui règle dynamiquement les paramètres de feedback du CSI (périodicité et type de CSI) en fonction de la vitesse de l’utilisateur. L’algorithme MABUC offre de très bonnes performances en termes de débit cible tout en réduisant efficacement la complexité et les coûts de signalisation CSI. Dans la dernière contribution de la thèse, nous approfondissons l’étude en explorant l’optimisation automatique des paramètres d’ordonnancement du CSI. Pour ce faire, nous exploitons l’outil de l’apprentissage par renforcement afin d’optimiser les paramètres de feedback CSI en fonction du profil de mobilité individuelle des utilisateurs. Plus spécifiquement, nous proposons deux modèles d’apprentissage. Le premier modèle basé sur un algorithme de type Q-learning a permis de démontrer l’efficacité de l’approche dans un scénario à taille réduite. Le second modèle, plus scalable car basé sur une approche Deep Q-learning, a été formulé sous la forme d’un processus de type POMDP (Partially observable Markov decision process). Les résultats montrent l’efficacité des solutions qui permettent de sélectionner les paramètres de feedback les plus adaptés à chaque profil de mobilité, même dans le cas complexe où chaque utilisateur possède un profil de mobilité différent et variable dans le temps. / Recently, mobile operators have been challenged by a tremendous growth in mobile data traffic. In such a context, Cloud Radio Access Network (CRAN) has been considered as a novel architecture for future wireless networks. The radio frequency signals from geographically distributed antennas are collected by Remote Radio Heads (RRHs) and transmitted to the cloud-centralized Baseband Units (BBUs) pool through fronthaul links. This centralized architecture enables a global optimization of joint baseband signal processing and radio resource management functions for all RRHs and users. At the same time, Heterogeneous Networks (HetNets) have emerged as another core feature for 5G network to enhance the capacity/coverage while saving energy consumption. Small cells deployment helps to shorten the wireless links to end-users and thereby improving the link quality in terms of spectrum efficiency (SE) as well as energy efficiency (EE). Therefore, combining both cloud computing and HetNet advantages results in the so-called Heterogeneous-Cloud Radio Access Networks (H-CRAN) which is regarded as one of the most promising network architectures to meet 5G and beyond system requirements. In this context, we address the crucial issue of beamforming and user-to-RRH association (user clustering) in the downlink of H-CRANs. We formulate this problem as a sum-rate maximization problem under the assumption of mobility and CSI (Channel State Information) imperfectness. Our main challenge is to design a framework that can achieve sum-rate maximization while, unlike other traditional reference solutions, being able to alleviate the computational complexity, CSI feedback and reassociation signaling costs under various mobility environments. Such gain helps in reducing the control and feedback overhead and in turn improve the uplink throughput. Our study begins by proposing a simple yet effective algorithm baptized Hybrid algorithm that periodically activates dynamic and static clustering schemes to balance between the optimality of the beamforming and association solutions while being aware of practical system constraints (complexity and signaling overhead). Hybrid algorithm considers time dimension of the allocation and scheduling process rather than its optimality (or suboptimality) for the sole current scheduling frame. Moreover, we provide a cost analysis of the algorithm in terms of several parameters to better comprehend the trade-off among the numerous dimensions involved in the allocation process. The second key contribution of our thesis is to tackle the beamforming and clustering problem from a mobility perspective. Two enhanced variants of the Hybrid algorithm are proposed: ABUC (Adaptve Beamforming and User Clustering), a mobility-aware version that is fit to the distinctive features of channel variations, and MABUC (Mobility-Aware Beamforming and User Clustering), an advanced version of the algorithm that tunes dynamically the feedback scheduling parameters (CSI feedback type and periodicity) in accordance with individual user velocity. MABUC algorithm achieves a targeted sum-rate performance while supporting the complexity and CSI signaling costs to a minimum. In our last contribution, we propose to go further in the optimization of the CSI feedback scheduling parameters. To do so, we take leverage of reinforcement learning (RL) tool to optimize on-the-fly the feedback scheduling parameters according to each user mobility profile. More specifically, we propose two RL models, one based on Q-learning and a second based on Deep Q-learning algorithm formulated as a POMDP (Partially observable Markov decision process). Simulation results show the effectiveness of our proposed framework, as it enables to select the best feedback parameters tailored to each user mobility profile, even in the difficult case where each user has a different mobility profile.

Pushing wireless data traffic onto small cells is important for alleviating congestion in the over-loaded macrocellular network. However, the ultimate potential of such load balancing and its effect on overall system performance is not well understood. With the ongoing deployment of multiple classes of access points (APs) with each class differing in transmit power, employed frequency band, and backhaul capacity, the network is evolving into a complex and “organic” heterogeneous network or HetNet. Resorting to system-level simulations for design insights is increasingly prohibitive with such growing network complexity. The goal of this dissertation is to develop realistic yet tractable frameworks to model and analyze load balancing dynamics while incorporating the heterogeneous nature of these networks. First, this dissertation introduces and analyzes a class of user-AP association strategies, called stationary association, and the resulting association cells for HetNets modeled as stationary point processes. A “Feller-paradox”-like relationship is established between the area of the association cell containing the origin and that of a typical association cell. This chapter also provides a foundation for subsequent chapters, as association strategies directly dictate the load distribution across the network. Second, this dissertation proposes a baseline model to characterize downlink rate and signal-to-interference-plus-noise-ratio (SINR) in an M-band K-tier HetNet with a general weighted path loss based association. Each class of APs is modeled as an independent Poisson point process (PPP) and may differ in deployment density, transmit power, bandwidth (resource), and path loss exponent. It is shown that the optimum fraction of traffic offloaded to maximize SINR coverage is not in general the same as the one that maximizes rate coverage. One of the main outcomes is demonstrating the aggressive of- floading required for out-of-band small cells (like WiFi) as compared to those for in-band (like picocells). To achieve aggressive load balancing, the offloaded users often have much lower downlink SINR than they would on the macrocell, particularly in co-channel small cells. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. As the third contribution, this dissertation proposes a tractable framework to analyze joint load balancing and resource partitioning in co-channel HetNets. Fourth, this dissertation investigates the impact of uplink load balancing. Power control and spatial interference correlation complicate the mathixematical analysis for the uplink as compared to the downlink. A novel generative model is proposed to characterize the uplink rate distribution as a function of the association and power control parameters, and used to show the optimal amount of channel inversion increases with the path loss variance in the network. In contrast to the downlink, minimum path loss association is shown to be optimal for uplink rate coverage. Fifth, this dissertation develops a model for characterizing rate distribution in self-backhauled millimeter wave (mmWave) cellular networks and thus generalizes the earlier multi-band offloading framework to the co-existence of current ultra high frequency (UHF) HetNets and mmWave networks. MmWave cellular systems will require high gain directional antennas and dense AP deployments. The analysis shows that in sharp contrast to the interferencelimited nature of UHF cellular networks, mmWave networks are usually noiselimited. As a desirable side effect, high gain antennas yield interference isolation, providing an opportunity to incorporate self-backhauling. For load balancing, the large bandwidth at mmWave makes offloading users, with reliable mmWave links, optimal for rate. / text

<p dir="ltr">Multi-omic studies ranging from genomics, transcriptomics (e.g., gene expression) to proteomics data exploration have been widely applied to interpret findings from genome wide association studies (GWAS) of Alzheimer's disease (AD). However, previous studies examine each -omics data type individually and the functional interactions between genetic variations, genes and proteins are only used after discovery to interpret the findings, but not beforehand. In this case, multi-omic findings are likely not functionally related and therefore it is challenging for result interpretation. To handle this challenge, we present new modularity constrained least absolute shrinkage and selection operator (M-LASSO), new modularity constrained logistic regression (M-Logistic), new interpretable multi-omic graph fusion neural network model (MoFNet) and new transfer learning framework integrated graph fusion neural network model (TransFuse) to integrate prior biological knowledge to model the functional interactions of multi-omic data. These approaches aim to identify functional connected sub-networks predictive of AD. In this thesis, the intrepretable model MoFNet and TransFuse incorporate prior biological connected multi-omics network, and for the first time model the dynamic information flow from deoxyribonucleic acid (DNA) to ribonucleic acid (RNA) and proteins. While applying the proposed models on multi-omic data from the religious orders study/memory and aging project (ROS/MAP) cohort, MoFNet and TransFuse outperformed all other state-of-art classifiers. Instead of targeting individual markers, the proposed methods identified multi-omic sub-networks associated with AD. MoFNet and TransFuse, produced sub-network and pathway findings that were robustly validated in another independent cohort. These identified gene/protein networks highlight potential pathways involved in AD pathogenesis and could offer systematic overview for understanding the molecular mechanisms of the disease. Investigating these identified pathways in more detail could help uncover the mechanisms causing synaptic dysfunction in AD and guide future research into potential therapeutic targets.</p>