# Capacity-Net-Based RIS Precoding Design without Channel Estimation for mmWave MIMO System

arXiv: https://arxiv.org/abs/2509.25660 · HTML: https://mlsanity.com/paper/2509.25660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM](https://mlsanity.com/paper/1808.05344/) (2018)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Spatial Deep Learning for Wireless Scheduling](https://mlsanity.com/paper/1808.01486/) (2018)
- [Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders](https://mlsanity.com/paper/2106.12271/) (2021)
- [End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks](https://mlsanity.com/paper/1709.03658/) (2017)
- [Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial](https://mlsanity.com/paper/2007.02759/) (2020)
- [Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation](https://mlsanity.com/paper/2009.14404/) (2020)
- [STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model](https://mlsanity.com/paper/2011.04292/) (2020)
- [Noisy-target Training: A Training Strategy for DNN-based Speech Enhancement without Clean Speech](https://mlsanity.com/paper/2101.08625/) (2021)
- [Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality](https://mlsanity.com/paper/1905.01898/) (2019)
- [CycleGAN-based Non-parallel Speech Enhancement with an Adaptive Attention-in-attention Mechanism](https://mlsanity.com/paper/2107.13143/) (2021)
- [Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication](https://mlsanity.com/paper/2009.01607/) (2020)
- [Channel Customization for Joint Tx-RISs-Rx Design in Hybrid mmWave Systems](https://mlsanity.com/paper/2109.13058/) (2021)
- [Low-Complexity Sum-Capacity Maximization for Intelligent Reflecting Surface-Aided MIMO Systems](https://mlsanity.com/paper/2204.04915/) (2022)

## Abstract (arXiv)

In this paper, we propose Capacity-Net, a novel unsupervised learning approach aimed at maximizing the achievable rate in reflecting intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple input multiple output (MIMO) systems. To combat severe channel fading of the mmWave spectrum, we optimize the phase-shifting factors of the reflective elements in the RIS to enhance the achievable rate. However, most optimization algorithms rely heavily on complete and accurate channel state information (CSI), which is often challenging to acquire since the RIS is mostly composed of passive components. To circumvent this challenge, we leverage unsupervised learning techniques with implicit CSI provided by the received pilot signals. Specifically, it usually requires perfect CSI to evaluate the achievable rate as a performance metric of the current optimization result of the unsupervised learning method. Instead of channel estimation, the Capacity-Net is proposed to establish a mapping among the received pilot signals, optimized RIS phase shifts, and the resultant achievable rates. Simulation results demonstrate the superiority of the proposed Capacity-Net-based unsupervised learning approach over learning methods based on traditional channel estimation.