The optimal scheduling of interfering links in a dense wireless network with
full frequency reuse is a challenging task. The traditional method involves
first estimating all the interfering channel strengths then optimizing the
scheduling based on the model. This model-based method is however resource
intensive and computationally hard because channel estimation is expensive in
dense networks; furthermore, finding even a locally optimal solution of the
resulting optimization problem may be computationally complex. This paper shows
that by using a deep learning approach, it is possible to bypass the channel
estimation and to schedule links efficiently based solely on the geographic
locations of the transmitters and the receivers, due to the fact that in many
propagation environments, the wireless channel strength is largely a function
of the distance dependent path-loss. This is accomplished by unsupervised
training over randomly deployed networks, and by using a novel neural network
architecture that computes the geographic spatial convolutions of the
interfering or interfered neighboring nodes along with subsequent multiple
feedback stages to learn the optimum solution. The resulting neural network
gives near-optimal performance for sum-rate maximization and is capable of
generalizing to larger deployment areas and to deployments of different link
densities. Moreover, to provide fairness, this paper proposes a novel
scheduling approach that utilizes the sum-rate optimal scheduling algorithm
over judiciously chosen subsets of links for maximizing a proportional fairness
objective over the network. The proposed approach shows highly competitive and
generalizable network utility maximization results.

本篇研究论文讨论如何使用深度学习方法，通过利用发射机和接收机之间的地理位置，不需要通道估计，对干扰链路的优化调度算法，提出了一种新的神经网络结构。并针对现有的基于模型的方法提出了相对公正的基于比例的调度方法。实际测试表明，该方法可适用于大型网络和不同链路密度的部署，并有效地提高了网络效用。