  In limited feedback multi-user multiple-input multiple-output (MU-MIMO)
cellular networks, users send quantized information about the channel
conditions to the associated base station (BS) for downlink beamforming.
However, channel quantization and beamforming have been treated as two separate
tasks conventionally, which makes it difficult to achieve global system
optimality. In this paper, we propose an augmented deep unfolding (ADU)
approach that jointly optimizes the beamforming scheme at the BSs and the
channel quantization scheme at the users. In particular, the classic WMMSE
beamformer is unrolled and a deep neural network (DNN) is leveraged to
pre-process its input to enhance the performance. The variational information
bottleneck technique is adopted to further improve the performance when the
feedback capacity is strictly restricted. Simulation results demonstrate that
the proposed ADU method outperforms all the benchmark schemes in terms of the
system average rate.
