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

arXiv: https://arxiv.org/abs/2507.02824 · HTML: https://mlsanity.com/paper/2507.02824/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial](https://mlsanity.com/paper/2007.02759/) (2020)
- [Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization](https://mlsanity.com/paper/1907.06002/) (2019)
- [Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures](https://mlsanity.com/paper/2201.03866/) (2022)
- [Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design](https://mlsanity.com/paper/1908.10734/) (2019)
- [Simultaneous Transmitting and Reflecting (STAR)-RIS for Harmonious Millimeter Wave Spectrum Sharing](https://mlsanity.com/paper/2301.10619/) (2023)

## Built on by (most cited first, 2 of 2)

- [Green Learning for STAR-RIS mmWave Systems with Implicit CSI](https://mlsanity.com/paper/2509.06820/) (2025)
- [Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI](https://mlsanity.com/paper/2509.12658/) (2025)

## Abstract (arXiv)

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.