论文标题
基于元学习的MU-MIMO波束形成的基于元学习的梯度下降算法
A Meta-Learning Based Gradient Descent Algorithm for MU-MIMO Beamforming
论文作者
论文摘要
多用户多输入多输出（MU-MIMO）波束形成设计通常被配制为非凸权加权总和速率（WSR）最大化问题，该问题已知是NP-HARD。该问题是通过迭代算法解决的，该算法遇到了缓慢的收敛性，或者是使用深度学习工具（需要耗时的预训练过程）来解决。在本文中，我们提出了基于低复杂性元学习的梯度下降算法。具有轻质体系结构的元网络用于学习自适应梯度下降更新规则，以直接优化波束形式。在迭代优化过程中对这个轻巧的网络进行了训练，我们称之为\ emph {slin solving}时培训}，它可以消除基于深度学习的解决方案的培训过程和数据依赖性。扩展的模拟表明，所提出的方法可实现与现有的基于学习的方法相比，与传统的WMMSe Algorith相比，在享受了较低的范围内，可以实现优越的WSR绩效。
Multi-user multiple-input multiple-output (MU-MIMO) beamforming design is typically formulated as a non-convex weighted sum rate (WSR) maximization problem that is known to be NP-hard. This problem is solved either by iterative algorithms, which suffer from slow convergence, or more recently by using deep learning tools, which require time-consuming pre-training process. In this paper, we propose a low-complexity meta-learning based gradient descent algorithm. A meta network with lightweight architecture is applied to learn an adaptive gradient descent update rule to directly optimize the beamformer. This lightweight network is trained during the iterative optimization process, which we refer to as \emph{training while solving}, which removes both the training process and the data-dependency of existing deep learning based solutions.Extensive simulations show that the proposed method achieves superior WSR performance compared to existing learning-based approaches as well as the conventional WMMSE algorithm, while enjoying much lower computational load.
