  This paper investigates the fronthaul compression problem in a user-centric
cloud radio access network, in which single-antenna users are served by a
central processor (CP) cooperatively via a cluster of remote radio heads
(RRHs). To satisfy the fronthaul capacity constraint, this paper proposes a
transform-compress-forward scheme, which consists of well-designed
transformation matrices and uniform quantizers. The transformation matrices
perform dimension reduction in the uplink and dimension expansion in the
downlink. To reduce the communication overhead for designing the transformation
matrices, this paper further proposes a deep learning framework to first learn
a suboptimal transformation matrix at each RRH based on the local channel state
information (CSI), and then to refine it iteratively. To facilitate the
refinement process, we propose an efficient signaling scheme that only requires
the transmission of low-dimensional effective CSI and its gradient between the
CP and RRH, and further, a meta-learning based gated recurrent unit network to
reduce the number of signaling transmission rounds. For the sum-rate
maximization problem, simulation results show that the proposed two-stage
neural network can perform close to the fully cooperative global CSI based
benchmark with significantly reduced communication overhead for both the uplink
and the downlink. Moreover, using the first stage alone can already outperform
the existing local CSI based benchmark.
