论文标题
可学习的模型驱动的性能预测和对不完美的MIMO系统的优化：框架和应用
Learnable Model-Driven Performance Prediction and Optimization for Imperfect MIMO System: Framework and Application
论文作者
论文摘要
多输入多输出系统的性能分析和优化的最先进的方案通常会降解，甚至在动态复杂方案中变得无效，具有未知干扰和通道状态信息（CSI）不确定性。为了适应具有挑战性的设置并更好地完成这些网络自动调整任务，我们在本文中提出了一个通用可学习的模型驱动框架。为了解释所提出的框架的工作原理，我们将正则化的零式预编码视为使用实例，并设计了一个轻巧的神经网络，用于基于粗模型驱动的近似值的总和速率和检测误差的完善预测。然后，我们以迭代方式估算了学到的预测变量的CSI不确定性，并在此基础上优化了发送正则化项，并随后接收功率缩放因子。提出了一种深层展开的基于梯度下降的算法，以实现功率扩展，这在收敛速度和鲁棒性之间取决于良好的权衡。
State-of-the-art schemes for performance analysis and optimization of multiple-input multiple-output systems generally experience degradation or even become invalid in dynamic complex scenarios with unknown interference and channel state information (CSI) uncertainty. To adapt to the challenging settings and better accomplish these network auto-tuning tasks, we propose a generic learnable model-driven framework in this paper. To explain how the proposed framework works, we consider regularized zero-forcing precoding as a usage instance and design a light-weight neural network for refined prediction of sum rate and detection error based on coarse model-driven approximations. Then, we estimate the CSI uncertainty on the learned predictor in an iterative manner and, on this basis, optimize the transmit regularization term and subsequent receive power scaling factors. A deep unfolded projected gradient descent based algorithm is proposed for power scaling, which achieves favorable trade-off between convergence rate and robustness.
