DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift

TL;DR

This paper explores using deep neural networks to efficiently optimize precoding in RIS-assisted mmWave MIMO systems, improving throughput with reduced computational complexity compared to traditional exhaustive search methods.

Contribution

The study introduces a DNN-based approach for codeword selection in RIS-aided mmWave MIMO systems, offering a practical and faster alternative to exhaustive search while maintaining near-optimal spectral efficiency.

Findings

DNN achieves sub-optimal spectral efficiency with reduced computation.

DNN maintains performance despite variations in user-RIS distance.

Proposed method outperforms traditional exhaustive search in efficiency.

Abstract

In this paper, the precoding design is investigated for maximizing the throughput of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with obstructed direct communication paths. In particular, a reconfigurable intelligent surface (RIS) is employed to enhance MIMO transmissions, considering mmWave characteristics related to line-of-sight (LoS) and multipath effects. The traditional exhaustive search (ES) for optimal codewords in the continuous phase shift is computationally intensive and time-consuming. To reduce computational complexity, permuted discrete Fourier transform (DFT) vectors are used for finding codebook design, incorporating amplitude responses for practical or ideal RIS systems. However, even if the discrete phase shift is adopted in the ES, it results in significant computation and is time-consuming. Instead, the trained deep neural network (DNN) is developed to facilitate faster codeword selection. Simulation results show that the DNN maintains sub-optimal spectral efficiency even as the distance between the end-user and the RIS has variations in the testing phase. These results highlight the potential of DNN in advancing RIS-aided systems.