  This paper studies the energy efficiency of the cloud radio access network
(C-RAN), specifically focusing on two fundamental and different downlink
transmission strategies, namely the data-sharing strategy and the compression
strategy. In the data-sharing strategy, the backhaul links connecting the
central processor (CP) and the base-stations (BSs) are used to carry user
messages -- each user's messages are sent to multiple BSs; the BSs locally form
the beamforming vectors then cooperatively transmit the messages to the user.
In the compression strategy, the user messages are precoded centrally at the
CP, which forwards a compressed version of the analog beamformed signals to the
BSs for cooperative transmission. This paper compares the energy efficiencies
of the two strategies by formulating an optimization problem of minimizing the
total network power consumption subject to user target rate constraints, where
the total network power includes the BS transmission power, BS activation
power, and load-dependent backhaul power. To tackle the discrete and nonconvex
nature of the optimization problems, we utilize the techniques of reweighted
$\ell_1$ minimization and successive convex approximation to devise provably
convergent algorithms. Our main finding is that both the optimized data-sharing
and compression strategies in C-RAN achieve much higher energy efficiency as
compared to the non-optimized coordinated multi-point transmission, but their
comparative effectiveness in energy saving depends on the user target rate. At
low user target rate, data-sharing consumes less total power than compression,
however, as the user target rate increases, the backhaul power consumption for
data-sharing increases significantly leading to better energy efficiency of
compression at the high user rate regime.
