This paper studies a deep learning approach for binary assignment problems in wireless networks, which identifies binary variables for permutation matrices. This poses challenges in designing a structure of a neural network and its training strategies for generating feasible assignment solutions. To this end, this paper develop a new Sinkhorn neural network which learns a non-convex projection task onto a set of permutation matrices. An unsupervised training algorithm is proposed where the Sinkhorn neural network can be applied to network assignment problems. Numerical results demonstrate the effectiveness of the proposed method in various network scenarios.

翻译：本文研究无线网络的二进制分配问题的深层学习方法,找出变异矩阵的二进制变量,这对设计神经网络的结构及其培训战略以产生可行的分配解决方案提出了挑战。为此,本文件开发了一个新的辛克霍恩神经网络,将非混凝土预测任务引入一套变异矩阵。在可将辛克霍恩神经网络应用于网络分配问题的地方,提出了一种不受监督的培训算法。数字结果表明,所提议的方法在各种网络情景中是有效的。