This paper studies a deep learning (DL) framework to solve distributed
non-convex constrained optimizations in wireless networks where multiple
computing nodes, interconnected via backhaul links, desire to determine an
efficient assignment of their states based on local observations. Two different
configurations are considered: First, an infinite-capacity backhaul enables
nodes to communicate in a lossless way, thereby obtaining the solution by
centralized computations. Second, a practical finite-capacity backhaul leads to
the deployment of distributed solvers equipped along with quantizers for
communication through capacity-limited backhaul. The distributed nature and the
nonconvexity of the optimizations render the identification of the solution
unwieldy. To handle them, deep neural networks (DNNs) are introduced to
approximate an unknown computation for the solution accurately. In consequence,
the original problems are transformed to training tasks of the DNNs subject to
non-convex constraints where existing DL libraries fail to extend
straightforwardly. A constrained training strategy is developed based on the
primal-dual method. For distributed implementation, a novel binarization
technique at the output layer is developed for quantization at each node. Our
proposed distributed DL framework is examined in various network configurations
of wireless resource management. Numerical results verify the effectiveness of
our proposed approach over existing optimization techniques.

本文研究了在无线网络中解决分布式非凸约束优化问题的深度学习框架，其中使用基于本地信息的有效状态分配。通过引入深度神经网络，将原始问题转化为基于非凸约束的 DNN 的训练任务，并开发了一种基于原始 - 对偶方法的约束训练策略，为分布式实现开发了一种新的输出二值化技术。实验结果证实了该方法的有效性。