Optimizing Wireless Networks with Deep Unfolding: Comparative Study on
  Two Deep Unfolding Mechanisms
Abuzar B. M. Adam Mohammed A. M. Elhassan Elhadj Moustapha Diallo
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.