Wireless Link Scheduling Over Recurrent Riemannian Manifolds
Wireless Link Scheduling Over Recurrent Riemannian Manifolds
复制标题
DOI：
10.1109/tvt.2022.3228212
复制
发表时间：
2023-04
影响因子：
6.8
通讯作者：
R. Shelim;A. Ibrahim
中科院分区：
文献类型：
--
作者：
R. Shelim;A. Ibrahim
在设备到设备 (D2D) 设置中用于调度潜在干扰通信对的深度学习模型需要数百到数千个的大量训练样本。一些动态网络，例如车辆网络，无法容忍与收集大量训练样本相关的等待时间。可以利用此类网络中通信对之间的时空相关性来减少学习阶段。在本文中，我们提出了一种基于统计循环单元（SRU）的黎曼几何循环神经网络（R-RNN）方法，用于无线链路调度。首先，我们将任何有限时间范围内每个 D2D 对周围的局部图表示为黎曼流形上的点序列，这要归功于将其拓扑表示为对称正定 (SPD) 矩阵。我们计算黎曼度量，即 Stein 度量，它是 D2D 对之间时间依赖性的合适度量。然后，我们使用所提出的 R-RNN 方法中的 Stein 度量来预测未来有限数量的连续时隙的链路调度决策。仿真结果表明，所提出的方法仅用 45 个训练样本就能达到最先进的性能。
Deep learning models for scheduling of potentially-interfering communication pairs, in device-to-device (D2D) settings, require large training samples in the order of hundreds to thousands. Some of the dynamic networks, such as vehicular networks, cannot tolerate the waiting time associated with gathering a large number of training samples. Spatio-temporal correlation among communication pairs in such networks can be utilized to reduce the learning phase. In this paper, we propose a Riemannian-geometric recurrent neural network (R-RNN) method based on statistical recurrent unit (SRU) for wireless link scheduling. First, we represent local graphs around each D2D pair in any finite time frame as a sequence of points on Riemannian manifold thanks to representing its topology as a symmetric positive definite (SPD) matrix. We compute the Riemannian metric, i.e., Stein metric, which are suitable measures of time-dependence among D2D pairs. Then we use the Stein metric in the proposed R-RNN method to forecast the link scheduling decisions for a finite number of successive time slots ahead. Simulation results reveal that the proposed method achieves promising performance against the state-of-the-arts with only 45 training samples.