Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks
Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks
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DOI：
10.1109/tsp.2020.2988255
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发表时间：
2020-01-01
影响因子：
5.4
通讯作者：
Ribeiro, Alejandro
中科院分区：
文献类型：
--
作者：
Eisen, Mark;Ribeiro, Alejandro
我们考虑在无线网络中的一组发送器和接收器之间最优分配资源的问题。由此产生的优化问题采用约束统计学习的形式，其中可以通过参数化资源分配策略以无模型的方式找到解。卷积神经网络结构是一种很有吸引力的参数化选择，因为它们的维度很小，并且不随网络规模而变化。介绍了随机边图神经网络(REGNN)，它对无线网络中衰落干扰模式形成的随机图进行卷积。基于REGNN的分配策略保留了一个重要的排列等方差性质，使它们易于转移到不同的网络。在此基础上，提出了一种无监督的无模型原始对偶学习算法来训练REGNN的权值。通过数值模拟，我们证明了REGNN相对于启发式基准所获得的良好性能以及它们的迁移能力。
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.