Accompanying Data to Paper "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks"

This work develops a novel power control framework for energy-efficient powercontrol in wireless networks. The proposed method is a new branch-and-boundprocedure based on problem-specific bounds for energy-efficiency maximizationthat allow for faster convergence. This enables to find the global solution forall of the most common energy-efficient power control problems with acomplexity that, although still exponential in the number of variables, is muchlower than other available global optimization frameworks. Moreover, thereduced complexity of the proposed framework allows its practicalimplementation through the use of deep neural networks. Specifically, thanks toits reduced complexity, the proposed method can be used to train an artificialneural network to predict the optimal resource allocation. This is in contrastwith other power control methods based on deep learning, which train the neuralnetwork based on suboptimal power allocations due to the large complexity thatgenerating large training sets of optimal power allocations would have withavailable global optimization methods. As a benchmark, we also develop a novelfirst-order optimal power allocation algorithm. Numerical results show that aneural network can be trained to predict the optimal power allocation policy.

本研究构建了一种新颖的功率控制框架，旨在实现无线网络中的高效能量管理。所提出的方法是一种基于特定问题边界的分支定界过程，该过程针对能量效率最大化，能够实现更快的收敛。这使得能够求解所有最常见的高效功率控制问题的全局解，其复杂性尽管在变量数量上仍呈指数增长，但远低于其他现有的全局优化框架。此外，该框架的降低复杂性使其能够通过深度神经网络的实际应用得到实施。具体而言，得益于其降低的复杂性，所提出的方法可以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，后者由于生成大量训练集所需的复杂度过高，因此基于次优功率分配来训练神经网络。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，神经网络可以训练以预测最佳功率分配策略。