BANYAN · Big dAta aNalYtics for radio Access Networks

Big dAta aNalYtics for radio Access Networks

With the increasing diversity and heterogeneity of mobile services used by people and machines, 4G/5G networks need to meet the growing variety of quality of service (QoS) requirements. Due to the limitation of current mobile technologies, promising new technologies were emerged to tackle such issues. Network slicing, technologically enabled by Network Function Virtualization (NFV), is a promising paradigm. It improves the flexibility and elasticity of mobile networks by dynamically forming and combining logic slicing to adapt to the fluctuation of different mobile service demands. A key enabler for network slicing is accurate data-driven models and the prediction of the Spatio-temporal dynamics of the mobile service traffic, which allow discovering knowledge relevant to the orchestration of slices and anticipating the need for their reconfiguration. Another important aspect that shall be considered is the increasing indoor traffic demand that more than 90% of mobile data traffic occurs within buildings; Therefore, most 5G RAN shall be deployed indoors to provide access points to the users. In the proximity of the indoor wireless access network (RAN), the demand for effective data-driven slice management is particularly critical. RAN must adapt to most of the capacity and demand changes related to each mobile service, and its performance is very important to users' QoS. Objectives: • Develop algorithms based on multivariate analysis and deep learning (DL) to forecast macroscopic spatial-temporal mobile service demands caused by user traffic and mobility; • Develop data analytics based geolocation algorithms to geo-locate and characterize in-building mobile traffic demands at a high level of detail (e.g., including the exact time, building, and floor where traffic is generated); and • Develop data analytics-driven mechanisms to proactively optimize the orchestration of virtualized 5G RAN resources, coordinate outdoor and indoor networks, and coordinate multi-RAT indoor networks. In addition to the above research objectives, the project also has the following (B) doctoral training objectives: • Train a group of 5 outstanding early-stage researchers (ESRs) for both academia and industry. • Establish a virtual European center of excellence for data-driven 5G RAN research that will exist well beyond the end of this project, reducing the fragmentation and facilitating long-term transnational and inter-sectoral collaborations.

Data: CORDIS, © European Union