Cross-Layer Software-Defined 5G Network

In the past few decades, the world has witnessed a rapid growth in mobile communication and reaped great benefits from it. Even though the fourth generation (4G) mobile communication system is just being deployed worldwide, proliferating mobile demands call for newer wireless communication technologies with even better performance. Consequently, the fifth generation (5G) system is already emerging in the research field. However, simply evolving the current mobile networks can hardly meet such great expectations, because over the years the infrastructures have generally become ossified, closed, and vertically constructed. Aiming to establish a new paradigm for 5G mobile networks, in this article, we propose a cross-layer software-defined 5G network architecture. By jointly considering both the network layer and the physical layer together, we establish the two software-defined programmable components, the control plane and the cloud computing pool, which enable an effective control of the mobile network from the global perspective and benefit technological innovations. Specifically, by the cross-layer design for software-defining, the logically centralized and programmable control plane abstracts the control functions from the network layer down to the physical layer, through which we achieve the fine-grained controlling of mobile network, while the cloud computing pool provides powerful computing capability to implement the baseband data processing of multiple heterogeneous networks. We discuss the main challenges of our architecture, including the fine-grained control strategies, network virtualization, and programmability. The architecture significantly benefits the convergence towards heterogeneous networks and it enables much more controllable, programmable and evolvable mobile networks.Comment: 9 pages, 5 figures, submitted to Mobile Networks & Application

MHCP: Multimedia Hybrid Cloud Computing Protocol and Architecture for Mobile Devices

[EN] Multimedia cloud computing has appeared as a very attractive environment for the business world in terms of providing cost-effective services with a minimum of entry costs and infrastructure requirements. There are some architecture proposals in the related literature, but there is no multimedia cloud computing architecture with hybrid features specifically designed for mobile devices. In this article, we propose a new multimedia hybrid cloud computing architecture and protocol. It merges existing private and public clouds and combines IaaS, SaaS and SECaaS cloud computing models in order to find a common platform to deliver real time traffic from heterogeneous multimedia and social networks for mobile users. The developed protocol provides suitable levels of QoS, while providing a secure and trusted cloud environment.Jimenez, JM.; Díaz Santos, JR.; Lloret, J.; Romero Martínez, JO. (2019). MHCP: Multimedia Hybrid Cloud Computing Protocol and Architecture for Mobile Devices. IEEE Network. 33(1):106-112. https://doi.org/10.1109/MNET.2018.1300246S10611233

A software-defined architecture for next-generation cellular networks

In the recent years, mobile cellular networks are undergoing fundamental changes and many established concepts are being revisited. New emerging paradigms, such as Software-Defined Networking (SDN), Mobile Cloud Computing (MCC), Network Function Virtualization (NFV), Internet of Things (IoT),and Mobile Social Networking (MSN), bring challenges in the design of cellular networks architectures. Current Long-Term Evolution (LTE) networks are not able to accommodate these new trends in a scalable and efficient way. In this paper, first we discuss the limitations of the current LTE architecture. Second, driven by the new communication needs and by the advances in aforementioned areas, we propose a new architecture for next generation cellular networks. Some of its characteristics include support for distributed content routing, Heterogeneous Networks(HetNets) and multiple Radio Access Technologies (RATs). Finally, we present simulation results which show that significant backhaul traffic savings can be achieved by implementing caching and routing functions at the network edge

To Talk or to Work: Energy Efficient Federated Learning over Mobile Devices via the Weight Quantization and 5G Transmission Co-Design

Federated learning (FL) is a new paradigm for large-scale learning tasks across mobile devices. However, practical FL deployment over resource constrained mobile devices confronts multiple challenges. For example, it is not clear how to establish an effective wireless network architecture to support FL over mobile devices. Besides, as modern machine learning models are more and more complex, the local on-device training/intermediate model update in FL is becoming too power hungry/radio resource intensive for mobile devices to afford. To address those challenges, in this paper, we try to bridge another recent surging technology, 5G, with FL, and develop a wireless transmission and weight quantization co-design for energy efficient FL over heterogeneous 5G mobile devices. Briefly, the 5G featured high data rate helps to relieve the severe communication concern, and the multi-access edge computing (MEC) in 5G provides a perfect network architecture to support FL. Under MEC architecture, we develop flexible weight quantization schemes to facilitate the on-device local training over heterogeneous 5G mobile devices. Observed the fact that the energy consumption of local computing is comparable to that of the model updates via 5G transmissions, we formulate the energy efficient FL problem into a mixed-integer programming problem to elaborately determine the quantization strategies and allocate the wireless bandwidth for heterogeneous 5G mobile devices. The goal is to minimize the overall FL energy consumption (computing + 5G transmissions) over 5G mobile devices while guaranteeing learning performance and training latency. Generalized Benders' Decomposition is applied to develop feasible solutions and extensive simulations are conducted to verify the effectiveness of the proposed scheme.Comment: submitted to MOBIHO

System architecture and deployment scenarios for SESAME: small cEllS coordinAtion for Multi-tenancy and Edge services

The surge of the Internet traffic with exabytes of data flowing over operators’ mobile networks has created the need to rethink the paradigms behind the design of the mobile network architecture. The inadequacy of the 4G UMTS Long term Evolution (LTE) and even of its advanced version LTE-A is evident, considering that the traffic will be extremely heterogeneous in the near future and ranging from 4K resolution TV to machine-type communications. To keep up with these changes, academia, industries and EU institutions have now engaged in the quest for new 5G technology. In this paper we present the innovative system design, concepts and visions developed by the 5G PPP H2020 project SESAME (Small cEllS coordinAtion for Multi-tenancy and Edge services). The innovation of SESAME is manifold: i) combine the key 5G small cells with cloud technology, ii) promote and develop the concept of Small Cells-as-a-Service (SCaaS), iii) bring computing and storage power at the mobile network edge through the development of non-x86 ARM technology enabled micro-servers, and iv) address a large number of scenarios and use cases applying mobile edge computing

MECPerf: An Application-Level Tool for Estimating the Network Performance in Edge Computing Environments

Edge computing is an emerging architecture in 5G networks where computing power is provided at the edge of the fixed network, to be as close as possible to the end users. Computation offloading, better communication latency, and reduction of traffic in the core network are just some of the possible benefits. However, the Quality of Experience (QoE) depends significantly on the network performance of the user device towards the edge server vs. cloud server, which is not known a priori and may generally change very fast, especially in heterogeneous, dense, and mobile deployments. Building on the emergence of standard interfaces for the installation and operation of thirdparty edge applications in a mobile network, such as the MultiAccess Edge Computing (MEC) under standardization at the European Telecommunications Standards Institute (ETSI), we propose MECPerf, a tool for user-driven network performance measurements. Bandwidth and latency on different network segments are measured and stored in a central repository, from where they can be analyzed, e.g., by application and service providers without access to the underlying network management services, for run-time resource optimization