# Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI

arXiv: https://arxiv.org/abs/2509.12658 · HTML: https://mlsanity.com/paper/2509.12658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation](https://mlsanity.com/paper/2009.14404/) (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)
- [Next Generation Advanced Transceiver Technologies for 6G and Beyond](https://mlsanity.com/paper/2403.16458/) (2024)
- [Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2005.00269/) (2020)
- [Deep Learning-based Phase Reconfiguration for Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2009.13988/) (2020)
- [Low-overhead Beam Training Scheme for Extremely Large-Scale RIS in Near-field](https://mlsanity.com/paper/2211.15910/) (2022)
- [Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond](https://mlsanity.com/paper/2401.01007/) (2023)
- [Deep Learning-Empowered Predictive Beamforming for IRS-Assisted Multi-User Communications](https://mlsanity.com/paper/2104.12309/) (2021)
- [DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift](https://mlsanity.com/paper/2507.02824/) (2025)
- [Energy-Efficient RIS-Aided Cell-Free Massive MIMO Systems: Application, Opportunities, and Challenges](https://mlsanity.com/paper/2412.17222/) (2024)
- [Pilot-to-Data Power Ratio in RIS-Assisted Multiantenna Communication](https://mlsanity.com/paper/2507.21785/) (2025)

## Abstract (arXiv)

In this paper, we propose a sustainable long short-term memory (LSTM)-based precoding framework for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) MIMO systems. Instead of explicit channel state information (CSI) estimation, the framework exploits uplink pilot sequences to implicitly learn channel characteristics, reducing both pilot overhead and inference complexity. Practical hardware constraints are addressed by incorporating the phase-dependent amplitude model of RIS elements, while a multi-label training strategy improves robustness when multiple near-optimal codewords yield comparable performance. Simulations show that the proposed design achieves over 90% of the spectral efficiency of exhaustive search (ES) with only 2.2% of its computation time, cutting energy consumption by nearly two orders of magnitude. The method also demonstrates resilience under distribution mismatch and scalability to larger RIS arrays, making it a practical and energy-efficient solution for sustainable 6G wireless networks.