Learning Wireless Power Allocation Through Graph Convolutional Regression Networks Over Riemannian Manifolds
Learning Wireless Power Allocation Through Graph Convolutional Regression Networks Over Riemannian Manifolds
复制标题
基于黎曼流形上的图卷积回归网络学习无线功率分配
DOI：
10.1109/tvt.2023.3325200
复制
发表时间：
2024-03
影响因子：
6.8
通讯作者：
R. Shelim;Ahmed S. Ibrahim
中科院分区：
文献类型：
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作者：
R. Shelim;Ahmed S. Ibrahim
最佳功率分配是使无线网络中的数据速率最大化的关键使能器。最近，已经引入了各种深度神经网络模型来预测设备到设备（D2D）网络中的功率分配。然而，它们需要大的训练样本（即，网络布局）。相反，在本文中，我们的目标是开发一种具有较少训练样本的功率分配学习模型，这在动态网络中至关重要（例如，车辆网络），需要快速学习功率分配。所提出的模型将基于欧几里得的网络布局和功率分配问题转换为黎曼（即，非欧几里德）流形，这表明需要较少的学习参数，因此学习时间较短。由于谱表示的对称正定（SPD）特性（即，Laplacian矩阵）的网络布局。特别是，我们提出了一个图卷积回归网络（GCRN），用于以无监督的方式预测黎曼流形上的功率分配。仿真结果表明，所提出的GCRN模型接近大规模网络中的最大网络速率，只有300个训练样本，而不是基于欧几里得的学习模型的10，000。
Optimum power allocation is a key enabler for maximizing data rate in wireless networks. Recently, various deep neural network models have been introduced for predicting power allocation in device-to-device (D2D) networks. However, they require large training samples (i.e., network layouts). On the contrary in this paper, we aim to develop a learning model for power allocation with fewer training samples, which is vital in dynamic networks (e.g., vehicular networks) with the need for fast learning of power allocation. The proposed model transforms Euclidean-based network layouts and power allocation problems into Riemannian (i.e., non-Euclidean) manifolds, which is shown to require fewer learning parameters and hence shorter learning time. Such transformation is possible thanks to the symmetric positive definite (SPD) property of spectral representation (i.e., Laplacian matrix) of network layouts. In particular, we propose a graph convolutional regression network (GCRN) for predicting power allocation over Riemannian manifolds in an unsupervised manner. Simulation results demonstrate that the proposed GCRN model approaches the maximum network rate in large-scale networks, with only 300 training samples as opposed to 10,000 in Euclidean-based learning models.