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Optimization theory assisted algorithms have received great attention forprecoding design in multiuser multiple-input multiple-output (MU-MIMO) systems.Although the resultant optimization algorithms are able to provide excellentperformance, they generally require considerable computational complexity,which gets in the way of their practical application in real-time systems. Inthis work, in order to address this issue, we first propose a framework fordeep-unfolding, where a general form of iterative algorithm induceddeep-unfolding neural network (IAIDNN) is developed in matrix form to bettersolve the problems in communication systems. Then, we implement the proposeddeepunfolding framework to solve the sum-rate maximization problem forprecoding design in MU-MIMO systems. An efficient IAIDNN based on the structureof the classic weighted minimum mean-square error (WMMSE) iterative algorithmis developed. Specifically, the iterative WMMSE algorithm is unfolded into alayer-wise structure, where a number of trainable parameters are introduced toreplace the highcomplexity operations in the forward propagation. To train thenetwork, a generalized chain rule of the IAIDNN is proposed to depict therecurrence relation of gradients between two adjacent layers in the backpropagation. Moreover, we discuss the computational complexity andgeneralization ability of the proposed scheme. Simulation results show that theproposed IAIDNN efficiently achieves the performance of the iterative WMMSEalgorithm with reduced computational complexity.

Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems