  Cloud RAN (C-RAN) is a promising enabler for distributed massive MIMO
systems, yet is vulnerable to its fronthaul congestion. To cope with the
limited fronthaul capacity, this paper proposes a hybrid analog-digital
precoding design that adaptively adjusts fronthaul compression levels and the
number of active radio-frequency (RF) chains out of the entire RF chains in a
downlink distributed massive MIMO system based on C-RAN architecture. Following
this structure, we propose an analog beamformer design in pursuit of maximizing
multi-user sum average data rate (sum-rate). Each element of the analog
beamformer is constructed based on a weighted sum of spatial channel covariance
matrices, while the size of the analog beamformer, i.e. the number of active RF
chains, is optimized so as to maximize the large-scale approximated sum-rate.
With these analog beamformer and RF chain activation, a regularized zero-
forcing (RZF) digital beamformer is jointly optimized based on the
instantaneous effective channel information observed through the given analog
beamformer. The effectiveness of the proposed hybrid precoding algorithm is
validated by simulation, and its design criterion is clarified by analysis.
