Energy Efficient Cloud Computing Based Radio Access Networks in 5G. Design and evaluation of an energy aware 5G cloud radio access networks framework using base station sleeping, cloud computing based workload consolidation and mobile edge computing

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS

An Integrated, Virtualized Joint Edge and Fog Computing System with Multi-RAT Convergence

Notably, developing an innovative architectural network paradigm is essential to address the technical challenging of 5G applications' requirements in a unified platform. Forthcoming applications will provide a wide range ofnetworking, computing and storage capabilities closer to the endusers.In this context, the 5G-PPP Phase two project named "5GCORAL:A 5G Convergent Virtualized Radio Access Network Living at the Edge" aims at identifying and experimentally validating which are the key technology innovations allowing for the development of a convergent 5G multi-RAT access based on a virtualized Edge and Fog architecture being scalable, flexible and interoperable with other domains including transport, core network and distant Clouds. In 5G-CORAL, an architecture is proposed based on ETSI MEC and ETSI NFV frameworks in a unified platform. Then, a set of exemplary use cases benefiting from Edge and Fog networks in near proximity of the end-user are proposed for demonstration on top of connected car, shopping mall and high-speed train platforms.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586

View on 5G Architecture: Version 2.0

The 5G Architecture Working Group as part of the 5GPPP Initiative is looking at capturing novel trends and key technological enablers for the realization of the 5G architecture. It also targets at presenting in a harmonized way the architectural concepts developed in various projects and initiatives (not limited to 5GPPP projects only) so as to provide a consolidated view on the technical directions for the architecture design in the 5G era. The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural concepts from 5GPPP Phase 1 projects and initiatives. Capitalizing on the architectural vision and framework set by the first version of the white paper, this Version 2.0 of the white paper presents the latest findings and analyses with a particular focus on the concept evaluations, and accordingly it presents the consolidated overall architecture design

Open Cell-less Network Architecture and Radio Resource Management for Future Wireless Communication Systems

In recent times, the immense growth of wireless traffic data generated from massive mobile devices, services, and applications results in an ever-increasing demand for huge bandwidth and very low latency, with the future networks going in the direction of achieving extreme system capacity and ultra reliable low latency communication (URLLC). Several consortia comprising major international mobile operators, infrastructure manufacturers, and academic institutions are working to develop and evolve the current generation of wireless communication systems, i.e., fifth generation (5G) towards a sixth generation (6G) to support improved data rates, reliability, and latency. Existing 5G networks are facing the latency challenges in a high-density and high-load scenario for an URLLC network which may coexist with enhanced mobile broadband (eMBB) services. At the same time, the evolution of mobile communications faces the important challenge of increased network power consumption. Thus, energy efficient solutions are expected to be deployed in the network in order to reduce power consumption while fulfilling user demands for various user densities. Moreover, the network architecture should be dynamic according to the new use cases and applications. Also, there are network migration challenges for the multi-architecture coexistence networks. Recently, the open radio access network (O-RAN) alliance was formed to evolve RANs with its core principles being intelligence and openness. It aims to drive the mobile industry towards an ecosystem of innovative, multi-vendor, interoperable, and autonomous RAN, with reduced cost, improved performance and greater agility. However, this is not standardized yet and still lacks interoperability. On the other hand, the cell-less radio access network (RAN) was introduced to boost the system performance required for the new services. However, the concept of cell-less RAN is still under consideration from the deployment point of view with the legacy cellular networks. The virtualization, centralization and cooperative communication which enables the cell-less RAN can further benefit from O-RAN based architecture. This thesis addresses the research challenges facing 5G and beyond networks towards 6G networks in regard to new architectures, spectral efficiency, latency, and energy efficiency. Different system models are stated according to the problem and several solution schemes are proposed and developed to overcome these challenges. This thesis contributes as follows. Firstly, the cell-less technology is proposed to be implemented through an Open RAN architecture, which could be supervised with the near real-time RAN intelligent controller (near-RT-RIC). The cooperation is enabled for intelligent and smart resource allocation for the entire RAN. Secondly, an efficient radio resource optimization mechanism is proposed for the cell-less architecture to improve the system capacity of the future 6G networks. Thirdly, an optimized and novel resource scheduling scheme is presented that reduces latency for the URLLC users in an efficient resource utilization manner to support scenarios with high user density. At the same time, this radio resource management (RRM) scheme, while minimizing the latency, also overcomes another important challenge of eMBB users, namely the throughput of those who coexist in such a highly loaded scenario with URLLC users. Fourthly, a novel energy-efficiency enhancement scheme, i.e., (3 × E) is designed to increase the transmission rate per energy unit, with stable performance within the cell-less RAN architecture. Our proposed (3 × E) scheme activates two-step sleep modes (i.e., certain phase and conditional phase) through the intelligent interference management for temporarily switching access points (APs) to sleep, optimizing the network energy efficiency (EE) in highly loaded scenarios, as well as in scenarios with lower load. Finally, a multi-architecture coexistence (MACO) network model is proposed to enable inter-connection of different architectures through coexistence and cooperation logical switches in order to enable smooth deployment of a cell-less architecture within the legacy networks. The research presented in this thesis therefore contributes new knowledge in the cellless RAN architecture domain of the future generation wireless networks and makes important contributions to this field by investigating different system models and proposing solutions to significant issues.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidenta: Matilde Pilar Sánchez Fernández.- Secretario: Alberto Álvarez Polegre.- Vocal: José Francisco Monserrat del Rí

Scalable Algorithms for Cloud Radio Access Network (C-RAN) Optimization

In the evolving scenario of 5G networks, resource allocation algorithms for the Cloud Radio Access Network (C-RAN) model have proven to be the key for managing ever increasing Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) for mobile networks while ensuring high Quality of Service (QoS). In Chapter 1 a brief overview of the main elements of the C-RAN and of the methodologies that are employed in this work is provided. In Chapter 2, an exact scalable methodology for a static traffic scenario, based on lexicographic optimization, is proposed for the solution of a multi-objective optimization problem to achieve, among other goals, the minimization of the number of active nodes in the C-RAN while supporting reliability and meeting latency constraints. The optimal solution of the most relevant objectives for networks of several tens of nodes is obtained in few tens of seconds of computational time in the worst case. For the least relevant objective a heuristic is developed, providing near optimal solutions in few seconds of computing time. In Chapter 3, an optimization framework for dynamic C-RAN reconfiguration is developed. The objective is to maintain C-RAN cost optimization, while minimizing the cost of virtual network function migration. Significant savings in terms of migrations (above 82% for primary virtual BBU functions and above 75% for backup virtual BBU functions) can be obtained with respect to a static traffic scenario, with execution time of the optimization algorithm below 20 seconds in the worst cases, making its application feasible for dynamic scenarios. In Chapter 4, an alternative Column Generation model formulation is developed, and the quality of the computed lower bounds is evaluated. Further extensions from this baseline (e.g. Column Generation based heuristics, exact Branch&Price algorithms) are left as future work. In Chapter 5, the main results achieved in this work are summarized, and several possible extensions are proposed