Hybrid precoding reduces the radio-frequency (RF) hardware burden in wideband massive MIMO-OFDM systems, but conventional optimization and deep neural network (DNN)-based approaches can incur high online complexity. This paper proposes XGB-SVD, a lightweight hybrid precoding framework that maps compact ray-tracing path parameters directly to analog beamforming codeword indices using cascade eXtreme Gradient Boosting (XGBoost). The cascade captures sequential beam-selection dependencies and transmit--receive propagation correlation, while the digital precoder and combiner are obtained from the reduced-dimensional equivalent channel using singular value decomposition (SVD). Evaluations over five DeepMIMO scenarios spanning sub-6-GHz and millimeter-wave bands show that XGB-SVD consistently outperforms the considered DNN baselines and approaches simultaneous orthogonal matching pursuit (SOMP) in several environments. Cross-scenario results further indicate improved transfer from propagation-diverse training environments, while XGB-SVD provides lower online computational and training costs than the DNN baselines.

翻译：暂无翻译