论文标题
灵活的无监督学习，用于大规模的MIMO子阵列混合边界
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming
论文作者
论文摘要
混合边界成形是提高大型MIMO系统能源效率的有前途的技术。特别是，亚阵列混合边界的成形可以通过减少相移数来进一步降低功耗。但是，由于子阵列连接的离散性质和移相量的离散性质，设计混合波束形成向量是一项复杂的任务。找到RF链和天线之间的最佳连接需要在大搜索空间中解决非凸面问题。此外，常规解决方案假设可以使用完美的CSI，这在实际系统中并非如此。因此，我们提出了一种新型的无监督学习方法，用于设计任何子阵列结构的混合边界，同时支持量化的相变和嘈杂的CSI。该拟议的体系结构的一个主要特征是不需要光束成型的代码簿，并且对神经网络进行了训练以考虑相变量的量化。仿真结果表明，所提出的深度学习解决方案可以达到比现有方法更高的总和。
Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is a complex task due to the discrete nature of the subarray connections and the phase-shift amounts. Finding the optimal connections between RF chains and antennas requires solving a non-convex problem in a large search space. In addition, conventional solutions assume that perfect CSI is available, which is not the case in practical systems. Therefore, we propose a novel unsupervised learning approach to design the hybrid beamforming for any subarray structure while supporting quantized phase-shifters and noisy CSI. One major feature of the proposed architecture is that no beamforming codebook is required, and the neural network is trained to take into account the phase-shifter quantization. Simulation results show that the proposed deep learning solutions can achieve higher sum-rates than existing methods.
