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A cloud radio access network (C-RAN) is a promising cellular network, whereindensely deployed multi-antenna remote-radio-heads (RRHs) jointly serve manyusers using the same time-frequency resource. By extremely high signalingoverheads for both channel state information (CSI) acquisition and data sharingat a baseband unit (BBU), finding a joint transmission strategy with asignificantly reduced signaling overhead is indispensable to achieve thecooperation gain in practical C-RANs. In this paper, we present a novel sparsejoint transmission (sparse-JT) method for C-RANs, where the number of transmitantennas per unit area is much larger than the active downlink user density.Considering the effects of noisy-and-incomplete CSI and the quantization errorsin data sharing by a finite-rate fronthaul capacity, the key innovation ofsparse-JT is to find a joint solution for cooperative RRH clusters, beamformingvectors, and power allocation to maximize a lower bound of the sum-spectralefficiency under the sparsity constraint of active RRHs. To find such asolution, we present a computationally efficient algorithm that guarantees tofind a local-optimal solution for a relaxed sum-spectral efficiencymaximization problem. By system-level simulations, we exhibit that sparse-JTprovides significant gains in ergodic spectral efficiencies compared toexisting joint transmissions.

Sparse Joint Transmission for Cloud Radio Access Networks with Limited Fronthaul Capacity