  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 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. A
major advantage of this method lies in its use of low-complexity learnable
systems in which the number of parameters scales linearly with respect to the
hidden layer size of embedded neural architectures and the product of the
number of transmitter and receiver antennas only, fully independent of the
number of transceivers in the network. We illustrate the superiority of our
method through an empirical study of our approach in comparison to WMMSE and
also analyze its robustness to changes in channel conditions and network size.
