Hybrid Precoding Design Based on Dual-Layer Deep-Unfolding Neural Network

Dual-layer iterative algorithms are generally required when solving resource allocation problems in wireless communication systems. Specifically, the spectrum efficiency maximization problem for hybrid precoding architecture is hard to solve by the single-layer iterative algorithm. The dual-layer penalty dual decomposition (PDD) algorithm has been proposed to address the problem. Although the PDD algorithm achieves significant performance, it requires high computational complexity, which hinders its practical applications in real-time systems. To address this issue, we first propose a novel framework for deep-unfolding, where a dual-layer deep-unfolding neural network (DLDUNN) is formulated. We then apply the proposed frame-work to solve the spectrum efficiency maximization problem for hybrid precoding architecture. An efficient DLDUNN is designed based on unfolding the iterative PDD algorithm into a layer-wise structure. We also introduce some trainable parameters in place of the high-complexity operations. Simulation results show that the DLDUNN presents the performance of the PDD algorithm with remarkably reduced complexity.