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Many innovative applications are projected to be supported by 5G networks across three verticals: enhanced mobile broadband, ultra-reliable low latency communication, and massive machine-type communication. Given the constraints of the current Radio Access Networks (RANs), accommodating all these applications, considering their Quality of Service and Quality of Experience (QoE) requirements, is not practical. OpenRAN is a new architecture touted as the most viable nextgeneration RAN solution. It promotes a software-defined component, labelled RAN Intelligent Controller (RIC), that governs and supplies intelligence to optimize radio resource allocation, implement handovers, manage interference, and balance load between cells. RIC has two parts: Non-Real-Time (RT) and Near-RT. This article introduces a novel QoE Enhancement Function (QoE2F) xApp to enhance the functionality of Near-RT RIC through providing efficient resource provisioning to users requesting high-resolution video services. It deploys an innovative Adaptive Genetic Algorithm to perform optimal user association along with resource and power allocation in HetNets. Simulation results demonstrate superior QoE2F xApp performance in terms of VMAF and MoS for two different resolution videos and diverse numbers of user

The Video traffic has seen a surge in the last decade due to the widespread use of smartphones and the abundance of video streaming applications in the market. Considering the time varying characteristics of today's networks, ensuring high quality of experience (QoE) to all video traffic users has become a daunting challenge for most service providers. The dynamic adaptive streaming over HTTP (DASH) standard enables the adjustment of video bitrates to match the network conditions, therefore guaranteeing smooth video playback. Different DASH-based approaches have been proposed. Nonetheless, most of these schemes incur substantial bitrate oscillations due to their quick reactions to changes in bandwidth, which negatively impact the users' QoE. In this paper, we propose EDRA, a DASH-based bitrate adaption solution that aims at averting video playback interrupts while reducing the number of bitrate switches. EDRA dynamically adjusts the bounds of available video bitrates based on bandwidth estimations. It then selects the most suitable bitrate for each video segment taking into consideration the current and previous bandwidth measurements, the buffer level and the bitrate variation with respect to the previously downloaded segments. Simulation results show that EDRA outperforms existing commercial schemes as it incurs between 6% and 22% higher accumulated played utility and between 30% and 77% lower bitrate switches, ensuring a smooth video streaming experience at high throughput levels.

—Achieving high Quality of Service (QoS) is one of the important goals in the latest 5G Heterogeneous Networks (HetNets) environments. However, ensuring fairness among users with Reduced Power Consumption (RPC) is a major challenge. Although several studies have examined the joint issue of User Association (UA), Resource Allocation (RA), and Power Allocation (PA), there is still no optimal solution that achieves QoS fairness and RPC with low complexity and processing time. This paper proposes the Power-Performance Efficient Adaptive Genetic Algorithm (P 2EAGA) for solving the UA-RA-PA problem in HetNets. Simulation results show that P 2EAGA outperforms existing schemes in terms of variability, fairness, RPC, and QoS, including throughput, packet loss ratio, delay, and jitter. Simulation results also show that P 2EAGA generates solutions that are very close to the optimal global solution compared to the Default Genetic Algorithm.

—One of the most important 5G features is their support for heterogeneous networks (HetNets). Complementing the classic macrocell base stations (MBS), femtocell base stations (FBS) are beneficial in terms of extensive coverage, including indoor, and enhancement of capacity. Unfortunately, FBSs performance in 5G HetNets is affected by complex cross-tier and co-tier interferences, causing reduced quality of service (QoS) and unfairness among users. This paper proposes an innovative resource allocation (RA) algorithm for interference mitigation (IM) based on graph coloring techniques to improve QoS and interuser fairness. The proposed algorithm, named Weighted EdgeWeighted Vertex Interference Mitigation (WEWVIM), employs a weight to the directed edge corresponding to the interference strength from nearby base stations (BSs) and a weight to every vertex, indicating the color with the smallest interference or higher transmission rate. A region of interest (ROI) is formed to find the interfering BSs. Simulation results show that WEWVIM outperforms existing schemes in terms of fairness and QoS, including throughput, packet loss ratio (PLR), delay, and jitter. Index Terms—HetNets, Graph Coloring, Interference Mitigation, 5G, QoS, Resource Allocation

The recent worldwide sanitary pandemic has made it clear that changes in user traffic patterns can create load balancing issues in networks (e.g., new peak hours of usage have been observed, especially in suburban residential areas). Such patterns need to be accommodated, often with reliable service quality. Although several studies have examined the user association and resource allocation (UA-RA) issue, there is still no optimal strategy to address such a problem with low complexity while reducing the time overhead. To this end, we propose Performance-Improved Reduced Search Space Simulated Annealing (P IRS3A), an algorithm for solving UA-RA problems in Heterogeneous Networks (HetNets). First, the UA-RA problem is formulated as a multiple 0/1 knapsack problem (MKP) with constraints on the maximum capacity of the base stations (BS) along with the transport block size (TBS) index. Second, the proposed P IRS3A is used to solve the formulated MKP. Simulation results show that P IRS3A outperforms existing schemes in terms of variability and Quality of Service (QoS), including throughput, packet loss ratio (PLR), delay, and jitter. Simulation results also show that P IRS3A generates solutions that are very close to the optimal solution compared to the default simulated annealing (DSA) algorithm.

This paper presents, describes and evaluates the Machine Learning Performance Monitor (MLPM), an innovative Machine Learning (ML) approach to fore cast and extrapolate the performance of several network features (e.g., latency, throughput) in a Multipath TCP(MPTCP) subflow pool. MLPM uses linear regression to predict the performance of network features along with Artificial Neural Network linear classifier to choose the best subflow (i.e., network path) capable of delivering the best performance to a given set of the network features. Results show that MLPM delivers better performance in terms of throughput and latency compared to existing schemes as it improves the MPTCP scheduler performance.

Les réseaux ad hoc de véhicules accueillent une multitude d’applications intéressantes. Parmi celles-ci, les applications d’info-divertissement visent à améliorer l’expérience des passagers. Ces applications ont des exigences rigides en termes de délai de livraison et de débit. De nombreuses approches ont été proposées pour assurer la qualité du service des dites applications. Elles sont réparties en deux couches : réseau et contrôle d’accès. Toutefois, ces méthodes présentent plusieurs lacunes. Cette thèse a trois volets. Le premier aborde la question du routage dans le milieu urbain. A cet égard, un nouveau protocole, appelé SCRP, a été proposé. Il exploite l’information sur la circulation des véhicules en temps réel pour créer des épines dorsales sur les routes et les connecter aux intersections à l’aide des nœuds de pont. Ces derniers collectent des informations concernant la connectivité et le délai, utilisées pour choisir les chemins de routage ayant un délai de bout-en-bout faible. Le deuxième s’attaque au problème d’affectation des canaux de services afin d’augmenter le débit. A cet effet, un nouveau mécanisme, appelé ASSCH, a été conçu. ASSCH collecte des informations sur les canaux en temps réel et les donne à un modèle stochastique afin de prédire leurs états dans l’avenir. Les canaux les moins encombrés sont sélectionnés pour être utilisés. Le dernier volet vise à proposer un modèle analytique pour examiner la performance du mécanisme EDCA de la norme IEEE 802.11p. Ce modèle tient en compte plusieurs facteurs, dont l’opportunité de transmission, non exploitée dans IEEE 802.11p., The fact that vehicular ad hoc network accommodates two types of communications, Vehicle-to-Vehicle and Vehicle-to-Infrastructure, has opened the door for a plethora of interesting applications to thrive. Some of these applications, known as infotainment applications, focus on enhancing the passengers' experience. They have rigid requirements in terms of delivery delay and throughput. Numerous approaches have been proposed, at medium access control and routing layers, to enhance the quality of service of such applications. However, existing schemes have several shortcomings. Subsequently, the design of new and efficient approaches is vital for the proper functioning of infotainment applications. This work proposes three schemes. The first is a novel routing protocol, labeled SCRP. It leverages real-time vehicular traffic information to create backbones over road segments and connect them at intersections using bridge nodes. These nodes are responsible for collecting connectivity and delay information, which are used to select routing paths with low end-to-end delay. The second is an altruistic service channel selection scheme, labeled ASSCH. It first collects real-time service channels information and feeds it to a stochastic model that predicts the state of these channels in the near future. The least congested channels are then selected to be used. The third is an analytical model for the performance of the IEEE 802.11p Enhanced Distributed Channel Access mechanism that considers various factors, including the transmission opportunity (TXOP), unexploited by IEEE 802.11p.