The fifth generation of mobile networks is envisioned to provide connectivity services to a multitude of devices with vastly different requirements. Current mobile systems rely on inflexible hardware-based RF front-end that provide a "onesize-fits-all" air-interface. Instead, future mobile networks should be flexible, providing different air-interfaces for particular users and applications. In this paper, we present HyDRA, a softwaredefined-radio virtualization layer that enables the execution of multiple programmable air-interfaces on top of one RF front-end. Our solution multiplexes digitized IQ signal samples of multiple virtual radios into a single stream. We have implemented HyDRA and experimentally evaluate its performance in a scenario that considers a base station executing LTE and NB-IoT VRs. Results obtained show that HyDRA is able to efficiently multiplex these two technologies, while the computational analysis shows that HyDRA is not CPU-intensive and can run in standard, commodity computers. We also show that HyDRA is a promising framework to enable RRH slicing, multi-radio access networks, and flexible multi-tenant networks.

To cope with the increasing number of co-existing wireless standards, complex machine learning techniques have been proposed for wireless technology classification. However, machine learning techniques in the scientific literature suffer from some shortcomings, namely: (i) they are often trained using data from only a single measurement location, and as such the results do not necessarily generalise and (ii) they typically do not evaluate complexity/accuracy trade-offs of the proposed solutions. To remedy these shortcomings, this paper investigates which resourcefriendly approaches are suitable across multiple heterogeneous environments. To this end, the paper designs and evaluates classifiers for LTE, Wi-Fi and DVB-T technologies using multiple datasets to investigate the complexity/accuracy trade-offs between manual feature extraction and automatic feature learning techniques. Our wireless technology classification reaches an accuracy up to 99%.

Network slicing is one of the key enabling techniques for 5G, allowing Mobile Network Operators (MNOs) to support services with diverging requirements on top of their infrastructure. The MNOs should be able to offer network slices as a service and provide customisable and independent virtual networks to verticals. The slicing of an end-to-end (E2E) mobile network is divided into Core Network (CN) slicing, and Radio Access Network (RAN) slicing. In this paper, we assess the requirements for using radio hypervisors to enable RAN as a Service (RANaaS). We evaluate the current state-of-the-art on radio virtualisation with respect to these requirements and identify the missing features. Then, we present the eXtensible Virtualisation Layer (XVL), a software layer that provides the missing functionality for enabling RANaaS and can be added on top of existing radio hypervisors. We outline XVL's architecture and design choices, as well as evaluate its performance in terms of the delay to provision virtual radios, the delay introduced to forward IQ samples, and the computational overhead. Our results show that XVL enables leveraging existing radio hypervisors to support RANaaS.

Abs tract. Increasingly popular, Internet applications for multimedia broadcasting require multipoint communication, in order to reduce network traffic rates. However, the widespread adoption of traditional multicast protocols is still held back by the current Internet structure, where the responsibility for management of multicast groups is distributed among network devices. By using distributed algorithms, such protocols generate delays in processing control groups events. In this paper we propose a clean-slate approach for multimedia multicasting, where the end to end calculation of the best route is performed to decrease delays in group configuration. The prototype developed implements this approach using OpenFlow technology. Results obtained through experimentation show a performance gain in relation to traditional IP multicasting.

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