Spatial Deep Learning for Wireless Scheduling

Abstract

The optimal scheduling of multiple interfering links in a densely deployed wireless network with full frequency reuse is a well-known challenging problem. The classical optimization approaches to this problem typically operate under the paradigm of first estimating all the interfering channel strengths then finding an optimum solution using the model. However, traditional scheduling methods are computationally and resource intensive, because channel estimation is expensive especially in dense networks, and further the optimization of link scheduling is typically a nonconvex problem. This paper takes a novel deep spatial learning approach to the scheduling problem. We show that it is possible to bypass the channel estimation stage altogether and to use a deep neural network to produce a near optimal schedule based solely on geographic locations of the transmitters and receivers in the network. This is accomplished by taking advantage of the recent advances in fractional programming that allows us to generate high- quality local optimum solutions to the scheduling problem for randomly deployed device-to-device networks as training data, and by using a novel neural network architecture that takes the geographic spatial convolutions of the interfering and interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution.