In this work, we conduct a comparative study on two deep unfolding mechanisms
to efficiently perform power control in the next generation wireless networks.
The power control problem is formulated as energy efficiency over multiple
interference links. The problem is nonconvex. We employ fractional programming
transformation to design two solutions for the problem. The first solution is a
numerical solution while the second solution is a closed-form solution. Based
on the first solution, we design a semi-unfolding deep learning model where we
combine the domain knowledge of the wireless communications and the recent
advances in the data-driven deep learning. Moreover, on the highlights of the
closed-form solution, fully deep unfolded deep learning model is designed in
which we fully leveraged the expressive closed-form power control solution and
deep learning advances. In the simulation results, we compare the performance
of the proposed deep learning models and the iterative solutions in terms of
accuracy and inference speed to show their suitability for the real-time
application in next generation networks.

在这项研究中，我们对两种深度展开机制进行了比较研究，以便在下一代无线网络中有效地进行功率控制。通过分数规划转换，我们设计了两个解决方案来解决非凸能效问题。第一个解决方案是数值解，而第二个解决方案是封闭形式的解决方案。基于第一个解决方案，我们设计了一个半展开深度学习模型，将无线通信领域的领域知识与数据驱动的深度学习的最新进展结合在一起。此外，我们基于封闭形式解决方案的亮点设计了一个完全展开的深度学习模型，充分利用了具有表达性的封闭形式功率控制解决方案和深度学习的进展。在模拟结果中，我们比较了所提出的深度学习模型和迭代解决方案在准确性和推理速度方面的性能，以展示它们在实时应用于下一代网络的适用性。