论文标题
基于学习的杂种杂交边缘用于毫米波多用户MIMO系统
Learning Based Hybrid Beamforming for Millimeter Wave Multi-User MIMO Systems
论文作者
论文摘要
混合波束形成（HBF）设计是毫米波（MMWave）多用户多输入多输出（MU-MIMO）系统的关键阶段。但是，常规的HBF方法仍然具有很高的复杂性，并且强烈依赖于通道状态信息的质量。我们提出了一个极端的学习机（ELM）框架，以共同优化传输和接收光束器。具体而言，为了提供用于培训的准确标签，我们首先提出了基于派系的基于派系编程和基于大型化的HBF方法（FP-MM-HBF）。然后，提出了一个基于ELM的HBF（ELM-HBF）框架来增加光束形成器的鲁棒性。与现有方法相比，FP-MM-HBF和ELM-HBF都可以提供更高的系统总和。此外，ELM-HBF不能只提供强大的HBF性能，还可以消耗很短的计算时间。
Hybrid beamforming (HBF) design is a crucial stage in millimeter wave (mmWave) multi-user multi-input multi-output (MU-MIMO) systems. However, conventional HBF methods are still with high complexity and strongly rely on the quality of channel state information. We propose an extreme learning machine (ELM) framework to jointly optimize transmitting and receiving beamformers. Specifically, to provide accurate labels for training, we first propose an factional-programming and majorization-minimization based HBF method (FP-MM-HBF). Then, an ELM based HBF (ELM-HBF) framework is proposed to increase the robustness of beamformers. Both FP-MM-HBF and ELM-HBF can provide higher system sum-rate compared with existing methods. Moreover, ELM-HBF cannot only provide robust HBF performance, but also consume very short computation time.
