Navegando por Assunto "Mobile networks"

Agora exibindo 1 - 3 de 3

Acesso aberto (Open Access)
Estratégia de planejamento e otimização do handover em redes móveis densificadas
(Universidade Federal do Pará, 2018-06-29) SILVA, Ketyllen da Costa; ARAÚJO, Jasmine Priscyla Leite de; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
The increase in mobile devices and applications in recent years has led to an overload of the network infrastructure responsible for disposing this traffic, thus affecting the performance of the network as the user experience.Heterogeneous mobile networks are already a reality and their densification has been put forward as one of the suggested solutions to meet the expected demands for 5th generation (5G) mobile networks. However, in the current networks, it is still common for fixed parameters to be used for the configuration of the network although this strategy does not always prove to be efficient. It is within this context that the concept of selforganizing networks (SONs) has been established, in which several network parameters are automatically adjusted on the basis of measurements and intelligent systems in real time. This thesis presents a strategy to optimize the handover in LTE networks with dense small cells. Based on measurements and fuzzy logic new algorithms are proposed for self-tuning network parameters. From discrete simulation using MATLAB, the results are evaluated and presented through the main performance metrics of handover.
Acesso aberto (Open Access)
Intent-based radio resource scheduling in ran slicing scenarios using reinforcement learning
(Universidade Federal do Pará, 2024-11-04) NAHUM, Cleverson Veloso; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization to deal with scarce and limited frequency spectrum resources while fulfilling the slice intents in an intent-based system. In this scenario, radio resource scheduling is an essential function to provide the resource management needed to prevent intent violations, hence providing sufficient radio resources for RAN slices to accomplish their intents. The wide variety of scenarios supported in 5G and beyond 5G (B5G) networks makes the radio resource scheduling (RRS) problem in RAN slicing scenarios a significant challenge. This thesis proposes an intent-based RRS for RAN slicing using reinforcement learning (RL) to fulfill the slice intent. The proposed method aims to prevent intent violations by making the management of resource block groups (RBGs) available between slices and users’ equipment (UEs) using inter-slice and intra-slice schedulers, respectively. This thesis also proposes investigating a slice prioritization structure to ensure the intent of more important slices when the available radio resources are insufficient to guarantee all slice’s intents. This thesis proposal presents results obtained using an intent-based RRS with RL for a fixed number of slices and also for multiple network scenarios, aiming to demonstrate the importance of intentbased RRS design for scenarios with RAN slicing. The proposed method outperformed the baselines in fixed and multiple network scenarios, protecting high-priority slices and minimizing the total number of violations.
Acesso aberto (Open Access)
Network slice admission using reinforcement learning and information-centric networking for mobile networks
(Universidade Federal do Pará, 2019-08-21) BATISTA, Pedro dos Santos; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
The evolution of the current most popular mobile network (4G), the so-called 5G, is targeting an increased traffic load at a lower cost. Thus, optimization of the delivery network plays an essential role at 5G; another aspect of the evolution is that 5G has the ambition to be highly customized, e.g., reliable enough to be used in industrial automation and cheap enough to be used for mobile broadband services. In this context, this thesis assesses two aspects of 5G: the first is to use information-centric networking (ICN) to improve the efficiency of multimedia delivery in mobile broadband services; and the second is the application of a reinforcement learning strategy as an enabler for the highly configurable network, which could pose a challenge to be understood and configured manually. ICN aims at circumventing several issues of current internet protocol, among them, achieving a more efficient multimedia distribution. Given the significant growth rate of video transmission over mobile networks, it is sensible to consider how mobile networks can leverage ICN. There is a substantial body of work considering ICN for fixed networks and also for the core of mobile networks. Less attention has been dedicated to ICN on the radio access network (RAN) or ICN-RAN, which has currently a user plane based on many connection-oriented protocols. To fully benefit from ICN, mobile networks must enable it on the RAN, not only on the core. This work details an ICN deployment on the RAN of the fourth and fifth generation of mobile networks and also presents a testbed that enables proofs of concept of this ICN-RAN using 4G. The results indicate, for example, that evolving ICN features can be tested with currently available tools, but the lack of hardware accelerators and optimized code limit the bit rate that can be achieved in real-time processing. In the context of network customization, the most prominent enablers are the so-called network slices. Slices can be understood as a part of the network that is customized to deliver certain services. The service requirements are imposed by the tenant, which acquire slices from an infrastructure provider. The 5G infrastructure provider must optimize the infrastructure resource utilization, usually admitting as many slices as possible. However, infrastructure resources are finite and admitting all the slices could increase the probability of service level agreement violation. This thesis investigates the application of reinforcement learning agents that learn how to increase the infrastructure provider revenue by intelligently admitting network slices that bring the most revenue to the system. We present a neural networks-driven agent for network slice admission that learns the characteristics of the slices deployed by the tenants from their resource requirements profile and balances the benefits of slice admission against orchestration and resource management costs.