Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI
This provides a practical and energy-efficient solution for sustainable 6G wireless networks, addressing hardware constraints and scalability issues in RIS systems.
The paper tackles the problem of high computational complexity and energy consumption in precoding for RIS-aided mmWave MIMO systems by proposing an LSTM-based framework that uses implicit CSI, achieving over 90% of the spectral efficiency of exhaustive search with only 2.2% of its computation time and cutting energy consumption by nearly two orders of magnitude.
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.