Radio hardware virtualization for software-defined wireless networks

Software-Defined Network (SDN) is a promising architecture for next generation Internet. SDN can achieve Network Function Virtualization much more efficiently than conventional architectures by splitting the data and control planes. Though SDN emerged first in wired network, its wireless counterpart Software-Defined Wireless Network (SDWN) also attracted an increasing amount of interest in the recent years. Wireless networks have some distinct characteristics compared to the wired networks due to the wireless channel dynamics. Therefore, network controllers present some extra degrees of freedom, such as taking measurements against interference and noise, or adapting channels according to the radio spectrum occupation. These specific characteristics bring about more challenges to wireless SDNs. Currently, SDWN implementations are mainly using customized firmware, such as OpenWRT, running on an embedded application processor in commercial WiFi chips, and restricted to layers above lower Media Access Control. This limitation comes from the fact that radio hardware usually require specific drivers, which have a proprietary implementation by various chipset vendors. Hence, it is difficult, if not impossible, to achieve virtualization on the radio hardware. However, this status has been changing as Software-Defined Radio (SDR) systems open up the entire radio communication stack to radio hobbyists and researchers. The bridge between SDR and SDN will make it possible to bring the softwarization and virtualization of wireless networks down to the physical layer, which will unlock the full potential of SDWN. This paper investigates the necessity and feasibility of extending the virtualization of wireless networks towards the radio hardware. A SDR architecture is presented for radio hardware virtualization in order to facilitate SDWN design and experimentation. We do believe that by adopting the virtualization-oriented hardware accelerator design presented here, an all-layer end-to-end high performance SDWN can be achieved

Fog-supported delay-constrained energy-saving live migration of VMs over multiPath TCP/IP 5G connections

The incoming era of the fifth-generation fog computing-supported radio access networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of virtual machines (VMs) toward nearby fog data centers. For this purpose, the bandwidths of the multiple wireless network interface cards of the wireless devices may be aggregated under the control of the emerging MultiPathTCP (MPTCP) protocol. However, due to the fading and mobility-induced phenomena, the energy consumptions of the current state-of-the-art VM migration techniques may still offset their expected benefits. Motivated by these considerations, in this paper, we analytically characterize and implement in software and numerically test the optimal minimum-energy settable-complexity bandwidth manager (SCBM) for the live migration of VMs over 5G FOGRAN MPTCP connections. The key features of the proposed SCBM are that: 1) its implementation complexity is settable on-line on the basis of the target energy consumption versus implementation complexity tradeoff; 2) it minimizes the network energy consumed by the wireless device for sustaining the migration process under hard constraints on the tolerated migration times and downtimes; and 3) by leveraging a suitably designed adaptive mechanism, it is capable to quickly react to (possibly, unpredicted) fading and/or mobility-induced abrupt changes of the wireless environment without requiring forecasting. The actual effectiveness of the proposed SCBM is supported by extensive energy versus delay performance comparisons that cover: 1) a number of heterogeneous 3G/4G/WiFi FOGRAN scenarios; 2) synthetic and real-world workloads; and, 3) MPTCP and wireless connections

Scalable RAN Virtualization in Multi-Tenant LTE-A Heterogeneous Networks (Extended version)

Cellular communications are evolving to facilitate the current and expected increasing needs of Quality of Service (QoS), high data rates and diversity of offered services. Towards this direction, Radio Access Network (RAN) virtualization aims at providing solutions of mapping virtual network elements onto radio resources of the existing physical network. This paper proposes the Resources nEgotiation for NEtwork Virtualization (RENEV) algorithm, suitable for application in Heterogeneous Networks (HetNets) in Long Term Evolution-Advanced (LTE-A) environments, consisting of a macro evolved NodeB (eNB) overlaid with small cells. By exploiting Radio Resource Management (RRM) principles, RENEV achieves slicing and on demand delivery of resources. Leveraging the multi-tenancy approach, radio resources are transferred in terms of physical radio Resource Blocks (RBs) among multiple heterogeneous base stations, interconnected via the X2 interface. The main target is to deal with traffic variations in geographical dimension. All signaling design considerations under the current Third Generation Partnership Project (3GPP) LTE-A architecture are also investigated. Analytical studies and simulation experiments are conducted to evaluate RENEV in terms of network's throughput as well as its additional signaling overhead. Moreover we show that RENEV can be applied independently on top of already proposed schemes for RAN virtualization to improve their performance. The results indicate that significant merits are achieved both from network's and users' perspective as well as that it is a scalable solution for different number of small cells.Comment: 40 pages (including Appendices), Accepted for publication in the IEEE Transactions on Vehicular Technolog

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

FPGA based technical solutions for high throughput data processing and encryption for 5G communication: A review

The field programmable gate array (FPGA) devices are ideal solutions for high-speed processing applications, given their flexibility, parallel processing capability, and power efficiency. In this review paper, at first, an overview of the key applications of FPGA-based platforms in 5G networks/systems is presented, exploiting the improved performances offered by such devices. FPGA-based implementations of cloud radio access network (C-RAN) accelerators, network function virtualization (NFV)-based network slicers, cognitive radio systems, and multiple input multiple output (MIMO) channel characterizers are the main considered applications that can benefit from the high processing rate, power efficiency and flexibility of FPGAs. Furthermore, the implementations of encryption/decryption algorithms by employing the Xilinx Zynq Ultrascale+MPSoC ZCU102 FPGA platform are discussed, and then we introduce our high-speed and lightweight implementation of the well-known AES-128 algorithm, developed on the same FPGA platform, and comparing it with similar solutions already published in the literature. The comparison results indicate that our AES-128 implementation enables efficient hardware usage for a given data-rate (up to 28.16 Gbit/s), resulting in higher efficiency (8.64 Mbps/slice) than other considered solutions. Finally, the applications of the ZCU102 platform for high-speed processing are explored, such as image and signal processing, visual recognition, and hardware resource management

CMCVT : a concurrent multi-channel virtual transceiver

State-of-the-art wireless Gateways (GW) used in Internet of Things (IoT) offer a single channel radio link, which limits the capabilities of the IoT network controlled by the GW, as the GW can only use a single channel at a time to communicate with the end-device(s). The quality of service (e.g., aggregate throughput, latency) offered by a single channel GW could be substantially improved by employing a multi-channel transceiver, which is capable of transmitting/receiving data on different radio channels simultaneously, particularly for larger wireless networks. However, current solutions available in both research and commercial communities only offer multi-channel receiver capabilities, and do not incorporate the multi-channel transmitter part. In addition, in terms of implementation, these multi-channel receivers duplicate single-channel hardware functionality. In this paper, for the first time, a novel concurrent multi-channel virtual transceiver is introduced. The virtual transceiver offers multi-channel capabilities and uses the same single-hardware hardware implementation for the Physical (PHY) layer by employing the virtualization technique. This new virtual transceiver concept is demonstrated for an IEEE 802.15.4 based 8 x 8 channel transceiver, implemented on an Field Programmable Gate Array (FPGA) of a modern Software Defined Radio and is compared with the existing duplication approach. The duplication approach consumes 9008 LUTs, and 12120 FFs, whereas the proposed approach occupies only 2959 LUTs and 2105 FFs, saving 67.15% LUTs and 82.63% FFs in comparison with the duplication approach. The experimental results reveal that the virtual transceiver provides the same performance (e.g., receiver sensitivity of -98.5dBm) as the transceiver achieved by duplicating the PHY layers but consumes much less hardware resources. (C) 2020 The Authors. Published by Elsevier GmbH

VNET: Towards End-to-End Network Cloudification

International audienceTo address the needs for future network services, existing network architecture should evolve significantly to provide a higher level of flexibility, resilience and quality of service. This challenge was also the one of computing which has found with " virtualization " a breakthrough approach to bring a high flexibility in existing computing architecture which makes today the success of cloud computing. The next obvious following step is therefore to " cloudify " the networks. We introduce the VNET project, which proposes to study some of the most complex network cloudification problems from a global point of view i.e at radio access networks and the core networks to the service hosting data centers. It will identify the challenges and address the problems related to the visualization of RAN (Radio Access Networks), the problems of service composition and dependability related to the deployment of SDN and NFV, and finally the design a NaaS platform with the associated tools to allow the specification, validation, deployment and management of on-demand end-to-end services. In addition, the project will address the manageability of this " cloudified " architecture using autonomic concepts based on the MAPE framework

6G White Paper on Edge Intelligence

In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments and experiences, urban computing and autonomous traffic settings. We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers. In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges and identify the key research questions for the development of the Intelligent Edge services. As a main outcome of this white paper, we envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge