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.

中文翻译：

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使用机器学习方法进行上行链路无线通信中的信道分配


这封信研究了上行链路无线通信系统中的信道分配问题。我们的目标是在整数信道分配约束下最大化所有用户的总速率。提供了一种基于凸优化的算法来获得最佳通道分配，其中每一步都获得封闭式解。由于基于凸优化的算法计算复杂度较高，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，原始问题转换为回归问题，通过卷积神经网络（CNN）、前馈神经网络（FNN）、随机森林和门控的集成来解决循环单元网络（GRU）。结果表明，机器学习方法大大减少了计算时间，同时略微影响了预测精度。