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

2025-09-16

AI Summary

To address the high pilot overhead, excessive computational complexity, and trade-off between energy efficiency (EE) and spectral efficiency (SE) in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, this paper proposes an LSTM-based implicit channel learning precoding framework. The framework eliminates explicit channel estimation by performing end-to-end learning of channel features directly from uplink pilot sequences. It innovatively introduces joint phase–amplitude modeling of RIS elements and a multi-label classification training strategy, enhancing robustness while respecting practical hardware constraints. Experimental results demonstrate that the proposed method achieves over 90% of the optimal SE using only 2.2% of the computation time required by exhaustive search, reduces energy consumption by nearly two orders of magnitude, and maintains stable performance under distribution shift and large-scale RIS configurations.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Implicit CSI learning for RIS-aided mmWave MIMO systems
• Reducing pilot overhead and computational complexity
• Addressing practical hardware constraints with energy efficiency

Innovation

• LSTM-based precoding with implicit CSI learning
• Incorporates phase-dependent RIS amplitude model
• Multi-label training for robustness and scalability