Exploiting flexible functional split in converged software defined access networks

5G targets to offer a huge network capacity to support the expected unprecedented traffic growth due mainly to mobile and machine-type services. Thus, the 5G access network has to comply with very challenging architectural requirements. Mobile network scalability is achieved by playing appropriately with the centralization of network functions and by applying the functional split introducing the fronthaul. Although more advantageous in terms of network management and performance optimization, low-layer functional split options require larger bandwidth and lower latency to be guaranteed by the fronthaul in the access network, while preserving other concurrent fiber-to-the-x services. Thus, advanced mechanisms for the efficient management of available resources in the access network are required to control jointly both radio and optical domains. Softwarized mobile and optical segments facilitate the introduction of dedicated protocols to enable the inter-working of the two control planes. This paper proposes a new cooperation scheme to manage the adaptive flexible functional split in 5G networks conditioned to the resource availability in the optical access network. Techniques for the accurate estimation of available bandwidth and the associated real-time selection of the best suitable functional split option are investigated. Results show that the proposed software defined converged approach to wavelength and bandwidth management guarantees the optimal allocation of optical resources. The triple exponential smoothing forecasting technique enables efficient coexistence of mobile fronthaul and fixed connectivity traffic in the network, reducing traffic impairments with respect to other well-known forecasting techniques, while keeping the same level of centralization.This work was partially supported by the Italian Government under CIPE resolution no. 135 (December 21, 2012), project INnovating City Planning through Information and Communication Technologies (INCIPICT) and by the EC through the H2020 5G-TRANSFORMER project (Project ID 761536)

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig