Joint Access-Backhaul Perspective on Mobility Management in 5G Networks

The ongoing efforts in the research development and standardization of 5G, by both industry and academia, have resulted in the identification of enablers (Software Defined Networks, Network Function Virtualization, Distributed Mobility Management, etc.) and critical areas (Mobility management, Interference management, Joint access-backhaul mechanisms, etc.) that will help achieve the 5G objectives. During these efforts, it has also been identified that the 5G networks due to their high degree of heterogeneity, high QoS demand and the inevitable density (both in terms of access points and users), will need to have efficient joint backhaul and access mechanisms as well as enhanced mobility management mechanisms in order to be effective, efficient and ubiquitous. Therefore, in this paper we first provide a discussion on the evolution of the backhaul scenario, and the necessity for joint access and backhaul optimization. Subsequently, and since mobility management mechanisms can entail the availability, reliability and heterogeneity of the future backhaul/fronthaul networks as parameters in determining the most optimal solution for a given context, a study with regards to the effect of future backhaul/fronthaul scenarios on the design and implementation of mobility management solutions in 5G networks has been performed.Comment: IEEE Conference on Standards for Communications & Networking, September 2017, Helsinki, Finlan

Will SDN be part of 5G?

For many, this is no longer a valid question and the case is considered settled with SDN/NFV (Software Defined Networking/Network Function Virtualization) providing the inevitable innovation enablers solving many outstanding management issues regarding 5G. However, given the monumental task of softwarization of radio access network (RAN) while 5G is just around the corner and some companies have started unveiling their 5G equipment already, the concern is very realistic that we may only see some point solutions involving SDN technology instead of a fully SDN-enabled RAN. This survey paper identifies all important obstacles in the way and looks at the state of the art of the relevant solutions. This survey is different from the previous surveys on SDN-based RAN as it focuses on the salient problems and discusses solutions proposed within and outside SDN literature. Our main focus is on fronthaul, backward compatibility, supposedly disruptive nature of SDN deployment, business cases and monetization of SDN related upgrades, latency of general purpose processors (GPP), and additional security vulnerabilities, softwarization brings along to the RAN. We have also provided a summary of the architectural developments in SDN-based RAN landscape as not all work can be covered under the focused issues. This paper provides a comprehensive survey on the state of the art of SDN-based RAN and clearly points out the gaps in the technology.Comment: 33 pages, 10 figure

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