This work develops a novel power control framework for energy-efficient power control in wireless networks. The proposed method is a new branch-and-bound procedure based on problem-specific bounds for energy-efficiency maximization that allow for faster convergence. This enables to find the global solution for all of the most common energy-efficient power control problems with a complexity that, although still exponential in the number of variables, is much lower than other available global optimization frameworks. Moreover, the reduced complexity of the proposed framework allows its practical implementation through the use of deep neural networks. Specifically, thanks to its reduced complexity, the proposed method can be used to train an artificial neural network to predict the optimal resource allocation. This is in contrast with other power control methods based on deep learning, which train the neural network based on suboptimal power allocations due to the large complexity that generating large training sets of optimal power allocations would have with available global optimization methods. As a benchmark, we also develop a novel first-order optimal power allocation algorithm. Numerical results show that a neural network can be trained to predict the optimal power allocation policy.

中文翻译：

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一种全球最优的节能功率控制框架及其在无线干扰网络中的有效实现

这项工作为无线网络中的节能功率控制开发了一种新颖的功率控制框架。所提出的方法是一种新的分支定界程序，它基于特定问题的能量效率最大化边界，允许更快的收敛。这使得能够找到所有最常见的节能功率控制问题的全局解决方案，其复杂性虽然仍然是变量数量的指数，但远低于其他可用的全局优化框架。此外，所提出框架的复杂性降低，允许通过使用深度神经网络实际实现。具体而言，由于其降低的复杂性，所提出的方法可用于训练人工神经网络以预测最佳资源分配。这与基于深度学习的其他功率控制方法形成对比，后者基于次优功率分配训练神经网络，因为使用可用的全局优化方法生成大型优化功率分配训练集会具有很大的复杂性。作为基准，我们还开发了一种新颖的一阶最优功率分配算法。数值结果表明，可以训练神经网络来预测最优功率分配策略。