论文标题
大型MIMO系统中的基于深度学习的多用户功率分配和混合编码
Deep Learning based Multi-User Power Allocation and Hybrid Precoding in Massive MIMO Systems
论文作者
论文摘要
本文提出了基于深度学习的功率分配（DL-PA）和用于多源多多输入多输出（MU-MMIMO）系统的混合编码技术。我们首先利用基于角度的混合预编码技术来减少RF链的数量和通道估计开销。然后，我们通过完全连接的深神经网络（DNN）开发DL-PA算法。 DL-PA有两个阶段：（i）通过基于粒子群优化的PA（PSO-PA）算法获得的最佳分配功率，脱机监督学习，（ii）受过训练的DNN在线权力预测。与计算昂贵的PSO-PA相比，DL-PA大大降低了运行时的98.6％-99.9％，同时又可以实现最佳的总和结果。它使DL-PA成为MU-Mimo Systems实时在线应用程序的有希望的算法。
This paper proposes a deep learning based power allocation (DL-PA) and hybrid precoding technique for multiuser massive multiple-input multiple-output (MU-mMIMO) systems. We first utilize an angular-based hybrid precoding technique for reducing the number of RF chains and channel estimation overhead. Then, we develop the DL-PA algorithm via a fully-connected deep neural network (DNN). DL-PA has two phases: (i) offline supervised learning with the optimal allocated powers obtained by particle swarm optimization based PA (PSO-PA) algorithm, (ii) online power prediction by the trained DNN. In comparison to the computationally expensive PSO-PA, it is shown that DL-PA greatly reduces the runtime by 98.6%-99.9%, while closely achieving the optimal sum-rate capacity. It makes DL-PA a promising algorithm for the real-time online applications in MU-mMIMO systems.
