Context-Aware Joint Routing & Scheduling for mm-Wave Backhaul / Access Networks

This paper studies the joint path and resource optimization problem for 5G Radio Access Networks (RANs) based on traffic load and user mobility prediction information, assuming an underlying densely deployed small cell multi-hop mm-Wave environment. The optimization problem introduced aims to benefit enhanced Mobile Broadband (eMBB) traffic and is decoupled into two sub-problems. A user-oriented path selection sub-problem based on a predictive traffic and mobility model; and subsequently a resource allocation sub-problem considering end-to-end Key Performance Indicators (KPIs) assuming different traffic types. The latter sub-problem is also solved in conjunction with pro-active caching of video segments at different radio access elements to relax the backhaul capacity and reduce the latency over the multi-hop densely deployed network. Numerical evaluations show significant improvements in signaling cost, spectrum utilization and throughput.