We study the problem of optimal power allocation in single-hop multi-antenna ad-hoc wireless networks. A standard technique to solve this problem involves optimizing a tri-convex function under power constraints using a block-coordinate-descent (BCD) based iterative algorithm. This approach, termed WMMSE, tends to be computationally complex and time consuming. Several learning-based approaches have been proposed to speed up the power allocation process. A recent work, UWMMSE, learns an affine transformation of a WMMSE parameter in an unfolded structure to accelerate convergence. In spite of achieving promising results, its application is limited to single-antenna wireless networks. In this work, we present a UWMMSE framework for power allocation in (multiple-input multiple-output) MIMO interference networks. Through an empirical study, we illustrate the superiority of our approach in comparison to WMMSE and also analyze its robustness to changes in channel conditions and network size.

中文翻译：

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用于战术 MIMO 的 ML 辅助功率分配

我们研究了单跳多天线自组织无线网络中的最优功率分配问题。解决此问题的标准技术涉及使用基于块坐标下降 (BCD) 的迭代算法在功率约束下优化三凸函数。这种称为 WMMSE 的方法往往计算复杂且耗时。已经提出了几种基于学习的方法来加速功率分配过程。最近的一项工作 UWMMSE 在展开结构中学习 WMMSE 参数的仿射变换以加速收敛。尽管取得了可喜的成果，但其应用仅限于单天线无线网络。在这项工作中，我们提出了一个 UWMMSE 框架，用于（多输入多输出）MIMO 干扰网络中的功率分配。通过实证研究，