A unified service-based capability exposure framework for closed-loop network automation

The ongoing quest for the tight integration of network operation and the network service provisioning initiated with the introduction of 5G often clashes with the capacity of current network architectures to provide means for such integration. Owing to the traditional design of mobile networks, which barely required a tight interaction, network elements offer capabilities for their continuous optimization just within their domain (eg, access, or core), allowing for a "silo-style" automation that falls short when aiming at closed-loop automation that embraces all the actors involved in the network, from network functions up to the service-provider network functions. To this end, in this article, we make the case for the network-wide capability exposure framework for closed-loop automation by (i) defining the different entities that shall expose capabilities, and (ii) discussing why the state of the art solutions are not enough to support this vision. Our proposed architecture, which relies on registration and discovery, and exposure functions, allows for enhanced use cases that are currently not possible with state of the art solution. We prove the feasibility of our solution by implementing it in a real-world testbed, employing Artificial Intelligence algorithms to close the loop for the management of the radio access network.Part of this work was performed in the context of the H2020 5G-MoNArch project (grant agreement no. 761445). The work of Marco Gramaglia has been partially funded by the H2020 5G-TOURS project (grant agreement no. 856950), and by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D projects 6G-CLARION-NFD, 6G-CLARION-OR, 6G-CLARION-SI, and 6G-CLARION-O

Overall 5G-MoNArch architecture and implications for resource elasticity

Proceeding of: 2018 European Conference on Networks and Communications (EuCNC), June 18-21, Ljubljana, SloveniaThe fifth generation (5G) of mobile and wireless communications networks aims at addressing a diverse set of use cases, services, and applications with a particular focus on enabling new business cases via network slicing. The development of 5G has thus advanced quickly with research projects and standardization efforts resulting in the 5G baseline architecture. Nevertheless, for the realization of native end-to-end (E2E) network slicing, further features and optimizations shall still be introduced. In this paper, essential building blocks and design principles of the 5G architecture will be discussed capitalizing on the innovations that are being developed in the 5G-MoNArch project. Furthermore, building on the concept of resource elasticity introduced by 5G-MoNArch and briefly resummarized in this paper, an elasticity functional architecture is presented where the architectural implications required for each of the three dimensions of elasticity are described, namely computational, orchestration-driven, and slice-aware elasticity.This work has been performed in the framework of the H2020 project 5G-MoNArch co-funded by the EU

Identifying 5G system enhancements: enabling technologies for multi-service networks

Proceeding of: 2018 IEEE Conference on Standards for Communications and Networking (CSCN)The fifth generation (5G) of mobile and wireless communications networks aims at addressing a diverse set of use cases, services, and applications with a particular focus on enabling new business cases via network slicing. The development of 5G has thus advanced quickly with research projects and standardization efforts resulting in the 5G baseline architecture. Nevertheless, for the realization of native end-to-end (E2E) network slicing, further features and optimizations shall still be introduced. In this paper, we provide a gap analysis of current 5G system (5GS) with respect to some specific enhancements and detail our insights on the enabling innovations that can fill the identified gaps. We will then discuss the essential building blocks and design principles of an evolved 5G baseline architecture capitalizing on the innovations that are being developed.This work has been performed in the framework of the H2020 project 5G-MoNArch co-funded by the EU

A flexible network architecture for 5G systems

In this paper, we define a flexible, adaptable, and programmable architecture for 5G mobile networks, taking into consideration the requirements, KPIs, and the current gaps in the literature, based on three design fundamentals: (i) split of user and control plane, (ii) service-based architecture within the core network (in line with recent industry and standard consensus), and (iii) fully flexible support of E2E slicing via per-domain and cross-domain optimisation, devising inter-slice control and management functions, and refining the behavioural models via experiment-driven optimisation. The proposed architecture model further facilitates the realisation of slices providing specific functionality, such as network resilience, security functions, and network elasticity. The proposed architecture consists of four different layers identified as network layer, controller layer, management and orchestration layer, and service layer. A key contribution of this paper is the definition of the role of each layer, the relationship between layers, and the identification of the required internal modules within each of the layers. In particular, the proposed architecture extends the reference architectures proposed in the Standards Developing Organisations like 3GPP and ETSI, by building on these while addressing several gaps identified within the corresponding baseline models. We additionally present findings, the design guidelines, and evaluation studies on a selected set of key concepts identified to enable flexible cloudification of the protocol stack, adaptive network slicing, and inter-slice control and management.This work has been performed in the framework of the H2020 project 5G-MoNArch co-funded by the E

D2.2 Draft Overall 5G RAN Design

This deliverable provides the consolidated preliminary view of the METIS-II partners on the 5 th generation (5G) radio access network (RAN) design at a mid-point of the project. The overall 5G RAN is envisaged to operate over a wide range of spectrum bands comprising of heterogeneous spectrum usage scenarios. More precisely, the 5G air interface (AI) is expected to be composed of multiple so-called AI variants (AIVs), which include evolved legacy technology such as Long Term Evolution Advanced (LTE-A) as well as novel AIVs, which may be tailored to particular services or frequency bands.Arnold, P.; Bayer, N.; Belschner, J.; Rosowski, T.; Zimmermann, G.; Ericson, M.; Da Silva, IL.... (2016). D2.2 Draft Overall 5G RAN Design. https://doi.org/10.13140/RG.2.2.17831.1424

METIS research advances towards the 5G mobile and wireless system definition

[EN] The Mobile and wireless communications Enablers for the Twenty-twenty Information Society (METIS) project is laying the foundations of Fifth Generation (5G) mobile and wireless communication system putting together the point of view of vendors, operators, vertical players, and academia. METIS envisions a 5G system concept that efficiently integrates new applications developed in the METIS horizontal topics and evolved versions of existing services and systems. This article provides a first view on the METIS system concept, highlights the main features including architecture, and addresses the challenges while discussing perspectives for the further research work.Part of this work has been performed in the framework of the FP7 project ICT-317669 METIS, which is partly funded by the European Commission. The authors would like to acknowledge the contributions of their colleagues in METIS with special thanks to Petar Popovski, Peter Fertl, David Gozalvez-Serrano, Andreas Hoglund, Zexian Li, and Krystian Pawlak. Also thanks to Josef Eichinger and Malte Schellmann for the fruitful discussions during the revision of this article.Monserrat Del Río, JF.; Mange, G.; Braun, V.; Tullberg, H.; Zimmermann, G.; Bulakci, O. (2015). METIS research advances towards the 5G mobile and wireless system definition. 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Automated Uplink Power Control Optimization in LTE-Advanced Relay Networks

Relaying is standardized in 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE)-Advanced Release 10 as a promising cost-efficient enhancement to existing radio access networks. Relay deployments promise to alleviate the limitations of conventional macrocell-only networks such as poor indoor penetration and coverage holes. However, to fully exploit the benefits of relaying, power control (PC) in the uplink should be readdressed. In this context, PC optimization should jointly be performed on all links, i.e., on the donor-evolved Node B (DeNB)-relay node (RN), the DeNB-user equipment (UE) link, and the RN–UE link. This ensures proper management of interference in the network besides attaining a receiver dynamic range which ensures the orthogonality of the single-carrier frequency-division multiple access (SC-FDMA) system. In this article, we propose an automated PC optimization scheme which jointly tunes PC parameters in relay deployments. The automated PC optimization can be based on either Taguchi’s method or a meta-heuristic optimization technique such as simulated annealing. To attain a more homogeneous user experience, the automated PC optimization scheme applies novel performance metrics which can be adapted according to the operator’s requirements. Moreover, the performance of the proposed scheme is compared with a reference study that assumes a scenario-specific manual learn-by-experience optimization. The evaluation of the optimization methods within the LTE-Advanced uplink framework is carried out in 3GPP-defined urban and suburban propagation scenarios by applying the standardized LTE Release 8 PC scheme. Comprehensive results show that the proposed automated PC optimization can provide similar performance compared to the reference manual optimization without requiring direct human intervention during the optimization process. Furthermore, various trade-offs can easily be achieved; thanks to the new performance metrics.Peer reviewe