The rising number of smart phone subscriptions, which are expected to reach 9.2 billion by 2020, combined with the explosive demand for mobile video, which is expected to grow around 13 times by 2019, will result in a ten-fold increase of mobile data traffic by 2020. To support the unprecedented demand for mobile data traffic, network providers seek novel radio access technologies (RATs) that boost physical-layer link capacity, e.g. massive antenna, utilize additional spectrum, e.g. mmWave radio, or further densify network topology, e.g. small cells. Although such technologies will bring the area spectral efficiency closer to its theoretical limits, most of the functions required to support mobile communications at the end terminals, such as digital signal processing (DSP), interference and mobility management, radio resource management (RRM), are still handled by a single point of attachment at the network edge. This network design and management approach has served the mobile operators well over the past few decades, playing a key role in maintaining network scalability and shifting network management (and processing) from the core to the access network. Besides, it has allowed meeting specific per-user Quality of Service (QoS) or Quality of Experience (QoE) targets.

To this end, the mobile network ecosystem has changed dramatically and now includes: i) a handful of network operators with heterogeneous RATs, ii) small cells that are randomly deployed by the end users, iii) a dense multitude of ubiquitous wireless objects, a.k.a. ‘things’, with diverse functional capabilities, iv) divergent traffic patterns among different networks, markets, and user types, and v) diverse processing, communication and storage capacity at the edge network. Besides, the operational capacity of the network core has dramatically increased due to the pooling of shared communication, computing and storage resources. This drastic transform of the mobile network ecosystem has revealed key vulnerabilities and performance limitations inherent to handling the connectivity of mobile terminals by a single point of attachment at the network edge, requiring new communication and information processing technologies towards the versatile objectives set by the 5G and Beyond mobile data network.

This special interest group aims at monitoring, developing, evaluating and implementing forward-thinking solutions in light of the challenges rising in 5G and Beyond Mobile Data networks, putting emphasis in topics that include, but are not limited to, the following areas:

• Architectures and techniques for massively distributed massive MIMO systems
• PHY and antenna design techniques for 5G and Beyond data networks
• Techniques for massively parallelized Digital Signal Processing in 5G and Beyond data networks
• Cloud-empowered Radio Resource Management algorithms in 5G and Beyond data networks
• Bearer-less plain IP core network architectures, techniques and experimental results
• Predictive and reactive network management platforms for centralized processing in in 5G and Beyond data networks
• Popular content placement algorithms and performance evaluation results
• Novel transmission strategies for popular content delivery in 5G and Beyond data networks
• Popular content placement and delivery strategies for improved QoE in 5G and Beyond data networks
• Network coding and waveform design for concurrent downlink transmissions in 5G and Beyond data networks
• Techniques for ultra-reliable data compression in massively distributed MIMO systems
• Terminal-tracking techniques in 5G and Beyond data networks
• Transmission profile selection in 5G and Beyond data networks
• Capacity boosting techniques for the 5G and Beyond fronthaul and backhaul
• Mobility management and flow control strategies in massively distributed MIMO systems
• Massive machine type communications in 5G and Beyond data networks
• Beamforming techniques for the mmWave band
• Novel architectures, techniques and applications for big data –driven network optimization in 5G and Beyond data networks
• Analysis and Optimization of Cloud-Empowered Network Architectures

Leaders: Dr. Dionysis Xenakis, Dr. Elli Kartsakli, Dr. Ferran Adelantado i Freixer