  The gains afforded by cloud radio access network (C-RAN) in terms of savings
in capital and operating expenses, flexibility, interference management and
network densification rely on the presence of high-capacity low-latency
fronthaul connectivity between remote radio heads (RRHs) and baseband unit
(BBU). In light of the non-uniform and limited availability of fiber optics
cables, the bandwidth constraints on the fronthaul network call, on the one
hand, for the development of advanced baseband compression strategies and, on
the other hand, for a closer investigation of the optimal functional split
between RRHs and BBU. In this chapter, after a brief introduction to signal
processing challenges in C-RAN, this optimal function split is studied at the
physical (PHY) layer as it pertains to two key baseband signal processing
steps, namely channel estimation in the uplink and channel encoding/ linear
precoding in the downlink. Joint optimization of baseband fronthaul compression
and of baseband signal processing is tackled under different PHY functional
splits, whereby uplink channel estimation and downlink channel encoding/ linear
precoding are carried out either at the RRHs or at the BBU. The analysis, based
on information-theoretical arguments, and numerical results yields insight into
the configurations of network architecture and fronthaul capacities in which
different functional splits are advantageous. The treatment also emphasizes the
versatility of deterministic and stochastic successive convex approximation
strategies for the optimization of C-RANs.
