论文标题
使用机器学习方法在上行链路无线通信中的频道分配
Channel Assignment in Uplink Wireless Communication using Machine Learning Approach
论文作者
论文摘要
这封信调查了上行无线通信系统中的渠道分配问题。我们的目标是最大化所有受整数通道分配约束的用户的总和率。提供了基于凸优化的算法以获得最佳通道分配，其中每个步骤都可以获得封闭形式的解决方案。由于基于凸优化的算法的计算复杂性很高，因此采用机器学习方法来获得计算有效的解决方案。更具体地说，数据是通过使用基于凸优化的算法生成的，并且原始问题将转换为回归问题，该问题通过卷积神经网络（CNN）（CNNS）的整合来解决，馈电神经网络（FNNS），随机森林和门控相光单位网络（GRUS）（GRUS）。结果表明，机器学习方法在很大程度上会以略有损害的预测准确性减少计算时间。
This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. A convex optimization based algorithm is provided to obtain the optimal channel assignment, where the closed-form solution is obtained in each step. Due to high computational complexity in the convex optimization based algorithm, machine learning approaches are employed to obtain computational efficient solutions. More specifically, the data are generated by using convex optimization based algorithm and the original problem is converted to a regression problem which is addressed by the integration of convolutional neural networks (CNNs), feed-forward neural networks (FNNs), random forest and gated recurrent unit networks (GRUs). The results demonstrate that the machine learning method largely reduces the computation time with slightly compromising of prediction accuracy.
