  The baseband-up centralization architecture of radio access networks (C-RAN)
has recently been proposed to support efficient cooperative communications and
reduce deployment and operational costs. However, the massive fronthaul
bandwidth required to aggregate baseband samples from remote radio heads (RRHs)
to the central office incurs huge fronthauling cost, and existing baseband
compression algorithms can hardly solve this issue. In this paper, we propose a
graphbased framework to effectively reduce fronthauling cost through properly
splitting and placing baseband processing functions in the network. Baseband
transceiver structures are represented with directed graphs, in which nodes
correspond to baseband functions, and edges to the information flows between
functions. By mapping graph weighs to computational and fronthauling costs, we
transform the problem of finding the optimum location to place some baseband
functions into the problem of finding the optimum clustering scheme for graph
nodes. We then solve this problem using a genetic algorithm with customized
fitness function and mutation module. Simulation results show that proper
splitting and placement schemes can significantly reduce fronthauling cost at
the expense of increased computational cost. We also find that cooperative
processing structures and stringent delay requirements will increase the
possibility of centralized placement.
