A deep neural network (DNN) based power control method that aims at solving the non-convex optimization problem of maximizing the sum rate of a fading multi-user interference channel is proposed. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for the power control problem. A key challenge in training a DNN for the power control problem is the lack of ground truth, i.e., the optimal power allocation is unknown. To address this issue, PCNet leverages the unsupervised learning strategy and directly maximizes the sum rate in the training phase. We then present PCNet+, which enhances the generalization capacity of PCNet by incorporating noise power as an input to the network. Observing that a single PCNet(+) does not universally outperform the existing solutions, we further propose ePCNet(+), a network ensemble with multiple PCNets(+) trained independently. Simulation results show that for the standard symmetric $K$ -user Gaussian interference channel, the proposed methods can outperform state-of-the-art power control solutions under a variety of system configurations. Furthermore, the performance improvement of ePCNet comes with a reduced computational complexity.

中文翻译：

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通过集成深度神经网络实现最佳功率控制

提出了一种基于深度神经网络（DNN）的功率控制方法，旨在解决最大化衰落多用户干扰信道总和率的非凸优化问题。为此，我们首先介绍 PCNet，它是一个多层全连接神经网络，专为功率控制问题而设计。针对功率控制问题训练 DNN 的一个关键挑战是缺乏基本事实，即最优功率分配是未知的。为了解决这个问题，PCNet 利用无监督学习策略，在训练阶段直接最大化总和率。然后我们展示了 PCNet+，它通过将噪声功率作为网络的输入来增强 PCNet 的泛化能力。观察到单个 PCNet(+) 并不普遍优于现有解决方案，我们进一步提出了 ePCNet(+)，一个具有多个独立训练的 PCNets(+) 的网络集成。仿真结果表明，对于标准对称$K$ -用户高斯干扰信道，所提出的方法可以在各种系统配置下优于最先进的功率控制解决方案。此外，ePCNet 的性能提升伴随着计算复杂度的降低。