We consider the problem of optimally allocating resources across a set of
transmitters and receivers in a wireless network. The resulting optimization
problem takes the form of constrained statistical learning, in which solutions
can be found in a model-free manner by parameterizing the resource allocation
policy. Convolutional neural networks architectures are an attractive option
for parameterization, as their dimensionality is small and does not scale with
network size. We introduce the random edge graph neural network (REGNN), which
performs convolutions over random graphs formed by the fading interference
patterns in the wireless network. The REGNN-based allocation policies are shown
to retain an important permutation equivariance property that makes them
amenable to transference to different networks. We further present an
unsupervised model-free primal-dual learning algorithm to train the weights of
the REGNN. Through numerical simulations, we demonstrate the strong performance
REGNNs obtain relative to heuristic benchmarks and their transference
capabilities.

本研究旨在利用随机边图神经网络 (REGNN) 在无线网络中实现最优资源分配，通过卷积神经网络构建 REGNN，利用无监督模型进行权重训练，并测试其性能和转移能力。