论文标题
增强深度展开，以在多细胞大型MIMO中进行下行链路束缚，反馈有限
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback
论文作者
论文摘要
在有限的反馈多用户多用户多输入多输出（MU-MIMO）蜂窝网络中，用户将有关通道条件的量化信息发送到关联的基站（BS）以进行下行链路束缚。但是，频道量化和波束形成通常被视为两个独立的任务，这使得难以实现全球系统最优性。在本文中，我们提出了一种增强的深入展开方法（ADU）方法，该方法共同优化了BSS和用户的频道量化方案的光束形成方案。特别是，经典的WMMSE波束形式是展开的，并且要使用深层神经网络（DNN）来预先处理其输入以提高性能。当严格限制反馈能力时，采用了各种信息瓶颈技术来进一步提高性能。仿真结果表明，所提出的ADU方法在系统平均值方面优于所有基准方案。
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.
