无需信道信息即可高效预编码，降低毫米波广播系统能耗
Green Learning for STAR-RIS mmWave Systems with Implicit CSI
- 直接利用上行导频信号训练，跳过信道估计与迭代优化
- 相比传统方法降低超四数量级浮点运算，保持相近频谱效率
- 适合资源受限的实时广播场景，部署轻量且节能
本文提出一种基于绿色学习（GL）的预编码框架，用于同时传输与反射的可重构智能表面（STAR-RIS）辅助毫米波（mmWave）MIMO广播系统。针对未来6G网络对环境可持续性的关注，该方案采用广播传输架构，在多用户共享相同信息场景下提升频谱效率，减少冗余传输与功耗。不同于需完美信道状态信息（CSI）和迭代计算的传统优化方法（如块坐标下降法，BCD），所提GL框架直接作用于上行导频信号，无需显式CSI估计。与依赖CSI标签训练的深度学习方法不同，该框架避免使用深度神经网络和反向传播，实现更轻量设计。尽管训练时依赖全CSI下的BCD生成监督信号，推理阶段完全无须CSI。GL融合子空间近似与校正偏置（Saab）、基于相关特征测试（RFT）的特征选择，以及梯度增强树（XGBoost）决策学习，联合预测STAR-RIS系数与发射预编码器。仿真表明，所提方法在频谱效率上媲美BCD与基于深度学习的模型，同时浮点运算量（FLOPs）降低超过四个数量级。该优势使其特别适用于能源与硬件受限的实时广播场景。
原文摘要 · Abstract (English)
In this paper, a green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) MIMO broadcasting systems. Motivated by the growing emphasis on environmental sustainability in future 6G networks, this work adopts a broadcasting transmission architecture for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption. Different from conventional optimization methods, such as block coordinate descent (BCD) that require perfect channel state information (CSI) and iterative computation, the proposed GL framework operates directly on received uplink pilot signals without explicit CSI estimation. Unlike deep learning (DL) approaches that require CSI-based labels for training, the proposed GL approach also avoids deep neural networks and backpropagation, leading to a more lightweight design. Although the proposed GL framework is trained with supervision generated by BCD under full CSI, inference is performed in a fully CSI-free manner. The proposed GL integrates subspace approximation with adjusted bias (Saab), relevant feature test (RFT)-based supervised feature selection, and eXtreme gradient boosting (XGBoost)-based decision learning to jointly predict the STAR-RIS coefficients and transmit precoder. Simulation results show that the proposed GL approach achieves competitive spectral efficiency compared to BCD and DL-based models, while reducing floating-point operations (FLOPs) by over four orders of magnitude. These advantages make the proposed GL approach highly suitable for real-time deployment in energy- and hardware-constrained broadcasting scenarios.
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