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

2025-07-03

AI Summary

In mmWave MIMO systems, the direct path is often blocked, and conventional RIS phase optimization relies on computationally prohibitive exhaustive search (ES) over continuous phase spaces. Method: This paper proposes a low-overhead DNN-based precoding design. It constructs a permuted discrete Fourier transform (DFT) codebook and jointly models the ideal and practical RIS amplitude–phase responses; a DNN is trained to directly predict the optimal RIS phase codeword. Contribution/Results: By operating over a discrete, structured codebook, the method avoids ES in continuous phase space, achieving stable spectral efficiency under dynamic user mobility. Simulations demonstrate near-optimal throughput at inference time, with significantly lower computational complexity than ES. The results validate the efficiency and robustness of DNNs for RIS-aided mmWave communications.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Maximize throughput in RIS-aided mmWave MIMO systems with blocked paths
• Reduce computational complexity in precoding design using DNN
• Improve spectral efficiency in practical RIS systems with varying distances

Innovation

• DNN for faster codeword selection
• Permuted DFT vectors for codebook design
• RIS enhances mmWave MIMO transmissions