A dynamic functional split in 5G radio access networks

The 3rd Generation Partnership Project (3GPP) proposes a centralized architecture for the 5G radio access network (RAN) in order to reduce costs and mitigate inter-cell interference, which helps to increase user data rates. However, the limited capacity of current fronthaul networks renders it impossible for many RANs to be fully centralized. Instead, the operators can opt for a partially centralized architecture, in which only some of the functions of the RAN's processing chain are centralized. Previous work has tackled the optimal selection of these functions in a static or semi- static manner. In this paper, we present a 5G RAN that is able to dynamically adapt the subset of centralized functions to maximize data rates at runtime. We analyze the dynamics of a dense 5G RAN to derive a maximum convergence time for the selection algorithms and show that a dynamic functional split significantly improves data rates with respect to statically centralized solutions.