  A cloud radio access network (C-RAN) is a promising cellular network, wherein
densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many
users using the same time-frequency resource. By extremely high signaling
overheads for both channel state information (CSI) acquisition and data sharing
at a baseband unit (BBU), finding a joint transmission strategy with a
significantly reduced signaling overhead is indispensable to achieve the
cooperation gain in practical C-RANs. In this paper, we present a novel sparse
joint transmission (sparse-JT) method for C-RANs, where the number of transmit
antennas per unit area is much larger than the active downlink user density.
Considering the effects of noisy-and-incomplete CSI and the quantization errors
in data sharing by a finite-rate fronthaul capacity, the key innovation of
sparse-JT is to find a joint solution for cooperative RRH clusters, beamforming
vectors, and power allocation to maximize a lower bound of the sum-spectral
efficiency under the sparsity constraint of active RRHs. To find such a
solution, we present a computationally efficient algorithm that guarantees to
find a local-optimal solution for a relaxed sum-spectral efficiency
maximization problem. By system-level simulations, we exhibit that sparse-JT
provides significant gains in ergodic spectral efficiencies compared to
existing joint transmissions.
