AI 中文总结
本研究针对下行多用户MIMO系统的信道预测问题，提出基于高斯混合模型与格拉姆-平方根分解的高效框架，引入参数缩减技术降低复杂度，其性能优于多种经典及神经网络基线，可实现与全CSI预测相当的效果。
AI 中文摘要
准确的信道状态信息（CSI）对下行链路（DL）多用户（MU）多输入多输出（MIMO）系统至关重要，反馈延迟和移动性会降低预编码性能，为确保可靠的波束成形和干扰抑制，需进行CSI预测。实际系统中，信令开销导致全CSI反馈常不可行，发射机依赖接收机上报的部分CSI。本研究提出一种基于高斯混合模型（GMM）的MIMO-正交频分复用（OFDM）信道预测框架，采用格拉姆-平方根分解，针对高维问题引入利用结构化协方差矩阵的高效参数缩减技术，大幅降低复杂度且无明显性能下降，该缩减技术基于格拉姆-平方根分解，即便全CSI可用仍具价值。仿真结果表明，GMM预测精度最高，能准确捕获底层信道子空间，这对有效的MU预编码至关重要；所提方法优于零阶保持（ZOH）、一阶保持（FOH）、线性最小均方误差（LMMSE）预测器等经典基线，以及先进的基于神经网络（NN）的预测器；值得注意的是，经参数缩减的部分CSI GMM达到了与全CSI预测相当的性能，凸显其在有限反馈下高效建模信道结构的能力。
英文摘要
Accurate channel state information (CSI) is critical for downlink (DL)-multi-user (MU)-multiple-input multiple-output (MIMO) systems, where feedback delays and mobility can degrade precoding performance. To ensure reliable beamforming and interference mitigation, CSI prediction is required. In practical systems, full CSI feedback is often infeasible due to signaling overhead, so transmitters rely on partial CSI reported by the receivers. In this work, we propose a Gaussian mixture model (GMM)-based prediction framework for MIMO-orthogonal frequency-division multiplexing (OFDM) channels under partial feedback using Gram-square-root factorization. To address the high dimensionality, we introduce an efficient parameter reduction technique that exploits structured covariance matrices, significantly lowering complexity without noticeable performance degradation. This reduction is based on the Gram-square-root factorization and remains of interest even when full CSI is available. Simulation results demonstrate that GMMs achieve the highest prediction accuracy and correctly capture the underlying channel subspaces, which is essential for effective MU-precoding. The proposed method outperforms classical baselines such as zero-order hold (ZOH), first-order hold (FOH), and linear minimum mean squared error (LMMSE) predictors, and an advanced neural network (NN)-based predictor. Notably, the parameter-reduced partial CSI GMM achieves performance comparable to that of full CSI prediction, highlighting its ability to efficiently model the channel structure under limited feedback.
CommentsThis work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
