The storage of frequently requested multimedia content at small-cell base stations (BSs) can reduce the load of macro-BSs without relying on high-speed backhaul links. In this work, the optimal operation of a system consisting of a cache-aided small-cell BS and a macro-BS is investigated for both offline and online caching settings. In particular, a binary fading one-sided interference channel is considered in which the small-cell BS, whose transmission is interfered by the macro-BS, has a limited-capacity cache. The delivery time per bit (DTB) is adopted as a measure of the coding latency, that is, the duration of the transmission block, required for reliable delivery. For offline caching, assuming a static set of popular contents, the minimum achievable DTB is characterized through information-theoretic achievability and converse arguments as a function of the cache capacity and of the capacity of the backhaul link connecting cloud and small-cell BS. For online caching, under a time-varying set of popular contents, the long-term (average) DTB is evaluated for both proactive and reactive caching policies. Furthermore, a converse argument is developed to characterize the minimum achievable long-term DTB for online caching in terms of the minimum achievable DTB for offline caching. The performance of both online and offline caching is finally compared using numerical results.

Yes / By 2020, next generation (5G) cellular networks are expected to support a 1000 fold traffic increase. To meet such traffic demands, Base Station (BS) densification through small cells are deployed. However, BSs are costly and consume over half of the cellular network energy. Meanwhile, Cloud Radio Access Networks (C-RAN) has been proposed as an energy efficient architecture that leverage cloud computing technology where baseband processing is performed in the cloud. With such an arrangement, more energy gains can be acquired through statistical multiplexing by reducing the number of BBUs used. This paper proposes a green Intelligent Traffic and Resource Elastic Energy (iTREE) scheme for C-RAN. In iTREE, BBUs are reduced by matching the right amount of baseband processing with traffic load. This is a bin packing problem where items (BS aggregate traffic) are to be packed into bins (BBUs) such that the number of bins used are minimized. Idle BBUs can then be switched off to save energy. Simulation results show that iTREE can reduce BBUs by up to 97% during off peak and 66% at peak times with RAN power reductions of up to 27% and 18% respectively compared with conventional deployments.

Le Cloud Radio Access Network (C-RAN) est une future direction dans les réseaux de communications sans fils pour déployer des systèmes cellulaires 4G et renforcer la migration des opérateurs vers la nouvelle génération 5G. En comparaison avec l'architecture traditionnelle des stations de base distribuées, l'architecture C-RAN apporte un lot d'avantages à l'opérateur: meilleure utilisation des ressources radio, flexibilité du réseau, minimisation de la puissance consommée et amenuisement des coûts de déploiement. Dans cette thèse, nous adressons le problème d'allocation dynamique des ressources et minimisation de la puissance des communications à liaison descendante dans le C-RAN. Notre recherche vise à allouer les ressources radio à des flux dynamiques d'utilisateurs, tout en trouvant les meilleures combinaisons entre points d'accès et unités de calculs, pour satisfaire la demande de trafic. Il s'agit en outre, d'un problème d'optimisation non linéaire et NP-difficile, comprenant plusieurs contraintes relatives aux demandes de ressources des utilisateurs, gestion d'interférences, capacités fixes des unités de calcul dans le Cloud et des liaisons de transport ainsi que la limitation de la puissance transmise maximale. Afin de surmonter la complexité inhérente à cette problématique du C-RAN, nous présentons différentes approches pour l'allocation dynamique des ressources en trois principales contributions. Les résultats de nos simulations prouvent l'efficacité de nos méthodes, comparé à celles existantes dans la littérature, en termes de taux de débit de satisfaction, nombre d'antennes actives, puissance consommée dans le Cloud, résilience et coût opérationnel du C-RAN. / Cloud Radio Access Network (C-RAN) is a future direction in wireless communications for deploying cellular radio access subsystems in current 4G and next-generation 5G networks. In the C-RAN architecture, BaseBand Units (BBUs) are located in a pool of virtual base stations, which are connected via a high-bandwidth low latency fronthaul network to Radio Remote Heads (RRHs). In comparison to standalone clusters of distributed radio base stations, C-RAN architecture provides significant benefits in terms of centralized resource pooling, network flexibility and cost savings. In this thesis, we address the problem of dynamic resource allocation and power minimization in downlink communications for C-RAN. Our research aims to allocate baseband resources to dynamic flows of mobile users, while properly assigning RRHs to BBUs to accommodate the traffic and network demands. This is a non-linear NP-hard optimization problem, which encompasses many constraints such as mobile users' resources demands, interference management, BBU pool and fronthaul links capacities, as well as maximum transmission power limitation. To overcome the high complexity involved in this problem, we present several approaches for resource allocation strategies and tackle this issue in three stages. Obtained results prove the efficiency of our proposed strategies in terms of throughput satisfaction rate, number of active RRHs, BBU pool processing power, resiliency, and operational budget cost.

Le Cloud RAN a été préconisé pour la 5G. Cependant, sa mise en place rencontre des difficultés notamment sur l'intégration du fronthaul, ce dernier généralement basé sur l’interface CPRI représente le segment situé entre la Digital Unit et la Radio Unit. Vu les contraintes de débit, de latence et de gigue sur cette interface, le multiplexage en longueur est la solution adéquate pour son transport. En revanche, les technologies radio recommandées pour la 5G augmenteront considérablement les débits CPRI, ce qui rend l’utilisation du WDM bas coût très difficile. Cette thèse traite quatre sujets principaux : L'introduction d'un canal de contrôle dans le CPRI permettrait la supervision de l'infrastructure WDM et l'accordabilité en longueurs d'onde des transceivers. L’impact de l’intégration de ce canal de contrôle dans le fronthaul est étudié dans le chapitre II. La radio analogique sur fibre peut améliorer de manière significative l'efficacité spectrale du fronthaul, permettant potentiellement le transport des interfaces 5G. Une étude approfondie sur le gain réel apporté par cette solution est rapportée dans le chapitre III. La compression du CPRI basée sur la quantification uniforme et non uniforme est également une solution pour améliorer l'efficacité spectrale du CPRI. Le chapitre IV démontre expérimentalement les taux de compression réalisables. Enfin, les nouveaux splits fonctionnels sont considérés comme une solution prometteuse pour la 5G. Deux nouvelles interfaces ont été identifiées pour les splits couche haute et couche basse. Une étude théorique et expérimentale de ces nouvelles interfaces est présentée dans le chapitre V. / Cloud Radio Access Network (RAN) was identified as a key enabler for 5G. Its deployment is however meeting multiple challenges notably in the fronthaul integration, the latter being the segment located between the Digital Unit and the Radio Unit generally based on CPRI. Giving its bit-rate, latency and jitter constrains, Wavelength Division Multiplexing (WDM) is the most adequate solution for its transport. However, the radio technologies recommended for 5G will drastically increase the CPRI bit-rate making its transport very challenging with low-cost WDM. This thesis deals with four main topics : The introduction of a control channel in the CPRI enables offering the WDM infrastructure monitoring and the wavelength tunability in the transceivers. The study of this control channel integration in the fronthaul link is reported in the second chapter as well as an investigation on the wireless transmission of CPRI. The use of Analog Radio over Fiber (A-RoF) can significantly improve the fronthaul spectral efficiency compared to CPRI-based fronthaul enabling, potentially, the transport of 5G interfaces. A thorough investigation on the actual gain brought by this solution is stated in the third chapter. CPRI compression based on uniform and non-uniform quantization is also a solution to enhance the CPRI spectral efficiency. The fourth chapter describes this solution and experimentally shows the achievable compression rates. Finally, establishing a new functional split in the radio equipment was considered as a promising solution for 5G. Two new interfaces have been identified for high and low layer functional splits. A theoretical and experimental study of these new interfaces is reported in the fifth chapter.

La densification de réseau à l'aide de petites cellules massivement déployées sur les zones macro-cellules, représente une solution prometteuse pour les réseaux mobiles 5G avenir pour faire face à l'augmentation du trafic mobile. Afin de simplifier la gestion de l'hétérogène du réseau d'accès radio (Radio Access Network RAN) qui résulte du déploiement massif de petites cellules, des recherches récentes et des études industrielles ont favorisé la conception de nouvelles architectures de RAN centralisés appelés comme Cloud-RAN (C-RAN), ou RAN virtuel (V-RAN), en incorporant les avantages du cloud computing et Network Functions Virtualization (NFV). Le projet de DynaRoC vise l'élaboration d'un cadre théorique de l'orchestration de ressources pour les C-RAN et dériver les limites de performance fondamentaux ainsi que les arbitrages entre les différents paramètres du système, et la conception de mécanismes d'orchestration de ressources dynamiques sur la base des conclusions théoriques à atteindre un équilibre de performance souhaité, en tenant compte des différents défis de conception. Le doctorant va étudier les mécanismes d'optimisation des ressources novatrices pour favoriser le déploiement de C-RAN, améliorer leur performance exploitant la technologie Network Functions Virtualization / Network densification using small cells massively deployed over the macro-cell areas, represents a promising solution for future 5G mobile networks to cope with mobile traffic increase. In order to simplify the management of the heterogeneous Radio Access Network (RAN) that results from the massive deployment of small cells, recent research and industrial studies have promoted the design of novel centralized RAN architectures termed as Cloud-RAN (C-RAN), or Virtual RAN (V-RAN), by incorporating the benefits of cloud computing and Network Functions Virtualization (NFV). The DynaRoC project aims at (1) developing a theoretical framework of resource orchestration for C-RAN and deriving the fundamental performance limits as well as the tradeoffs among various system parameters, and (2) designing dynamic resource orchestration mechanisms based on the theoretical findings to achieve a desired performance balance, by taking into account various design challenges. The PhD student will investigate innovative resource optimization mechanisms to foster the deployment of C-RANs, improving their performance exploiting the enabling Network Functions Virtualization technology

Yes / Fifth generation (5G) cellular networks will be comprised of millions of connected devices like wearable devices, Androids, iPhones, tablets and the Internet of Things (IoT) with a plethora of applications generating requests to the network. The 5G cellular networks need to cope with such sky-rocketing tra c requests from these devices to avoid network congestion. As such, cloud radio access networks (C-RAN) has been considered as a paradigm shift for 5G in which requests from mobile devices are processed in the cloud with shared baseband processing. Despite call admission control (CAC) being one of radio resource management techniques to avoid the network congestion, it has recently been overlooked by the community. The CAC technique in 5G C-RAN has a direct impact on the quality of service (QoS) for individual connections and overall system e ciency. In this paper, a novel Fuzzy-Logic based CAC scheme with pre-emption in C-RAN is proposed. In this scheme, cloud bursting technique is proposed to be used during congestion, where some delay tolerant low-priority connections are pre-empted and outsourced to a public cloud with a penalty charge. Simulation results show that the proposed scheme has low blocking probability below 5%, high throughput, low energy consumption and up to 95% of return on revenue.

The Cloud Radio Access Network (RAN) is a groundbreaking technology employed in the telecommunications industry, offering flexible, scalable, and cost-effective solutions for seamless wireless network services. However, testing Cloud RAN applications presents significant challenges due to their complexity, potentially leading to delays and increased costs. A paramount solution to overcome these obstacles is test automation. Automating the testing process not only dramatically reduces manual efforts but also enhances testing accuracy and efficiency, expediting the delivery of high-quality products. In the current era of cutting-edge advancements, artificial intelligence (AI) and machine learning (ML) play a transformative role in revolutionizing Cloud RAN testing. These innovative technologies enable rapid identification and resolution of complex issues, surpassing traditional methods. The objective of this thesis is to adopt an AI-enabled approach to Cloud RAN test automation, harnessing the potential of machine learning and natural language processing (NLP) techniques to automatically select test cases from test instructions. Through thorough analysis, relevant keywords are extracted from the test instructions using advanced NLP techniques. The performance of three keyword extraction methods is compared, with SpaCy proving to be the superior keyword extractor. Using the extracted keywords, test script prediction is conducted through two distinct approaches: using test script names and using test script contents. In both cases, Random Forest emerges as the top-performing model, showcasing its effectiveness with diverse datasets, regardless of oversampling or undersampling data augmentation techniques. Based on the rule-based approach, the predicted test scripts are utilized to determine the order of execution among the predicted test scripts. The research findings highlight the significant impact of AI and ML techniques in streamlining test case selection and automation for Cloud RAN applications. The proposed AI-enabled approach optimizes the testing process, resulting in faster product delivery, reduced manual workload, and overall cost savings.

There is an ever-increasing usage of mobile data with global traffic having reached 115 exabytes per month at the end of 2022 for mobile data traffic including fixed wireless access. This is projected to grow up to 453 exabytes at the end of 2028, according to Ericssons 2022 mobile data traffic outlook report. To meet the increasing demand radio access networks (RAN) used for mobile communication are continuously being improved with the current generation enabling larger virtualization of the network through the Cloud RAN (C-RAN) architecture. This facilitates the usage of commercial off-the-shelf servers (COTS) in the network replacing specialized hardware servers and making it easier to scale up or down the network capacity after traffic demand. This thesis looks at how we can efficiently identify servers needed to meet traffic demand in a network consisting of both COTS servers and specialized hardware servers while trying to reduce the energy consumption of the network. We model the problem as a network where the antennas and radio heads are connectedto the core network through a C-RAN and a specialized hardware layer. The network is then represented using a graph where the nodes represent servers in the network. Using this problem model as a base we then generate problem instances with varying topologies, server profiles, and traffic demands. To find out how the traffic should be passed through the network we test two different methods: A mixed integer linear programming (MILP) method focused on energy minimization and a graph attention network (GAT) predictor combined with the energy minimization MILP. To help evaluate the results we also create three other methods: a MILP model that tries to spread the traffic as evenly as possible, a random predictor combined with the energy minimization MILP and a greedy method. Our results show that the energy optimization MILP method can be used to create optimal solutions, but it suffer from a slow computation time compared to the other methods. The GAT model shows promising results in making predictions regarding what servers should be included in a network making it possible to reduce the problem size and solve it faster with MILP. The mean energy cost of the solutions created using the combined GAT/MILP method was 4% more than just using MILP but the time gain was substantial for problems of similar size as the GAT was trained on. With regards to computation time the combined GAT/MILP method used was 85% faster than using only MILP. For networks of almost double the size than the ones that the GAT model was trained on the solutions of the combined GAT and MILP methods had a mean energy cost increase of 7% while still showing a strong speedup, being 93% faster than when only using MILP.

Many organizations have realized the importance of managing what they know in a proper way, with the sharing of knowledge as one of the most central aspects. However, the practices of knowledge sharing are seldom fully understood, and thus implementations of technical systems intended to improve knowledge sharing have less effect than expected. This master's thesis project identifies that the case company – the well-known, Swedish telecom giant Ericsson – suffers some potential knowledge gaps regarding its knowledge sharing practices, and the project thus serves the purpose to identify how and why employees indulge in sharing, with emphasis on the informal and employee-driven knowledge sharing.  To understand and find ways to improve the knowledge sharing practices, the thesis project performs a case study in the rapidly expanding Ericsson Cloud RAN project. The qualitative approach of Multi-Grounded Theory is used, to focus on the perceived situation as described by the employees, with previous research as a second grounding-point. The qualitative data is collected through literature analysis, semi-structured interviews, and exploration of the digital platforms and tools internally used to share and document knowledge. The research identifies that the Cloud RAN project needs improvements of the knowledge sharing culture, and create a norm to reuse the documented knowledge. In spite of this, many employees appreciate shared knowledge and contribute to the common good of knowledge. A joy in helping others, personal benefits, and contributions to a greater good drive many employees to share. On the flipside are barriers such as a high bar for contributions from a technical point of view, and a risk of limited reuse of knowledge. As reuse is key to make use of knowledge sharing, the master's thesis report contains identified mechanisms realized in mock-up versions of tools and websites. There, emphasis is put on the importance of having proper tools and access to a contact network to navigate the documented knowledge. Summarized, the findings suggest to utilize the enthusiasm for knowledge sharing among employees for a supportive role, to champion a refined culture and lower the bar to contribute. Frequent reminders of available tools and the impact of sharing what is known in a good way, alongside creating awareness of the direction of the organization, can create company-wide awareness and motivations for improving knowledge sharing.