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.

翻译：我们研究了单速多ANTANNA ad-hoc无线网络的最佳电力分配问题。解决这个问题的标准技术是利用基于区块坐标白(BCD)的迭代算法,在电力限制下优化三分形功能。这种方法,称为WMMSE, 往往在计算上复杂而耗时。我们提出了几种基于学习的方法来加快电力分配过程。最近的一项工作,UWMMSE, 学习了在一个展开的结构中对WMMSE参数的近距离转换,以加速融合。尽管取得了有希望的成果,但它的应用仅限于单一无线网络。在这项工作中,我们提出了一个UWMMSE框架,用于在(多投入的多重产出)MIMO干扰网络中进行电力分配。我们通过一项经验研究,展示了我们的方法相对于WMMSE的优势,并分析了它对于频道条件和网络规模变化的稳健性。