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.

翻译：本文研究了以用户为中心的云无线接入网中的前传压缩问题，其中单天线用户通过一组远程射频头（RRH）协作由中央处理器（CP）提供服务。为满足前传容量约束，本文提出了一种变换-压缩-转发方案，该方案由精心设计的变换矩阵和均匀量化器组成。变换矩阵在上行链路中实现降维，在下行链路中实现升维。为降低设计变换矩阵的通信开销，本文进一步提出一种深度学习框架：首先基于本地信道状态信息（CSI）在每个RRH处学习次优变换矩阵，然后通过迭代对其进行优化。为促进优化过程，我们提出一种高效信令方案，仅需在CP与RRH间传输低维有效CSI及其梯度；此外，还引入基于元学习的门控循环单元网络以减少信令传输轮次。针对和速率最大化问题，仿真结果表明，所提出的两阶段神经网络在显著降低上行和下行通信开销的同时，性能接近基于全局CSI的完全协作基准。此外，仅使用第一阶段即可超越现有基于本地CSI的基准。