论文标题
角度功率谱估计的混合数据和模型驱动算法
Hybrid data and model driven algorithms for angular power spectrum estimation
论文作者
论文摘要
我们提出了两种算法，这些算法使用模型和数据集来估算大型MIMO系统中的通道协方差矩阵的角度谱图。第一种算法是解决层次问题的迭代定点方法。它使用模型知识将候选角功率谱缩小到与测量协方差矩阵一致的集合。然后，从该集合中，相对于从数据中学到的希尔伯特式度量，该算法选择具有最小距离的角功率谱，其期望值最小。第二种算法解决了一个求解器在非负方块程序中的单个应用程序解决的替代优化问题。通过融合从数据集和模型获得的信息，这两种算法都可以根据模型胜过现有的方法，并且它们在环境变化和小数据集方面也很强。
We propose two algorithms that use both models and datasets to estimate angular power spectra from channel covariance matrices in massive MIMO systems. The first algorithm is an iterative fixed-point method that solves a hierarchical problem. It uses model knowledge to narrow down candidate angular power spectra to a set that is consistent with a measured covariance matrix. Then, from this set, the algorithm selects the angular power spectrum with minimum distance to its expected value with respect to a Hilbertian metric learned from data. The second algorithm solves an alternative optimization problem with a single application of a solver for nonnegative least squares programs. By fusing information obtained from datasets and models, both algorithms can outperform existing approaches based on models, and they are also robust against environmental changes and small datasets.
