用LSTM隐式学习信道，大幅降低6G系统能耗与开销
Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI
- 通过上行导频隐式学习信道特征，避免显式信道估计
- 仅需2.2%计算时间即达穷举搜索90%频谱效率
- 适配硬件限制，抗分布偏移，适合大规模智能表面场景
本文提出一种基于长短期记忆网络（LSTM）的可持续预编码框架，用于可重构智能表面（RIS）辅助的毫米波（mmWave）MIMO系统。该框架不依赖显式信道状态信息（CSI）估计，而是利用上行导频序列隐式学习信道特性，显著降低导频开销与推理复杂度。通过引入考虑RIS单元相位相关幅度特性的实际硬件模型，并采用多标签训练策略，提升了在多个近优码字性能相近时的鲁棒性。仿真表明，所提方案在仅消耗穷举搜索（ES）2.2%计算时间的情况下，实现超过90%的频谱效率，能耗降低近两个数量级。方法在分布不匹配条件下仍具韧性，且可扩展至更大规模的RIS阵列，为可持续6G无线网络提供高效可行解决方案。
原文摘要 · Abstract (English)
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.
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