To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division duplex (FDD) systems, the CSI is acquired through a feedback signal from the user equipment (UE). However, this may lead to an important overhead in FDD massive MIMO systems. Focusing on these systems, in this study, we propose a novel strategy to design the CSI feedback. Our strategy allows to optimally design variable length feedback, that is promising compared to fixed feedback since users experience channel matrices differently sparse. Specifically, principal component analysis (PCA) is used to compress the channel into a latent space with adaptive dimensionality. To quantize this compressed channel, the feedback bits are smartly allocated to the latent space dimensions by minimizing the normalized mean squared error (NMSE) distortion. Finally, the quantization codebook is determined with k-means clustering. Numerical simulations show that our strategy improves the zero-forcing beamforming sum rate by 17%, compared to CsiNetPro. The number of model parameters is reduced by 23.4 times, thus causing a significantly smaller offloading overhead. At the same time, PCA is characterized by a lightweight unsupervised training, requiring eight times fewer training samples than CsiNetPro.

翻译：为充分挖掘多输入多输出（MIMO）网络的潜力，基站（BS）需要获取下行信道状态信息（CSI）。在频分双工（FDD）系统中，CSI通过用户设备（UE）的反馈信号获取，但这在FDD大规模MIMO系统中可能导致显著的开销。针对这些系统，本文提出了一种新的CSI反馈设计策略。该策略能够优化设计可变长度反馈，相较于固定长度反馈具有优势，因为不同用户经历的稀疏信道矩阵存在差异。具体而言，采用主成分分析（PCA）将信道压缩至自适应维度的潜在空间。为量化该压缩信道，通过最小化归一化均方误差（NMSE）失真，将反馈比特智能地分配给潜在空间各维度。最后，利用k-means聚类确定量化码本。数值仿真表明，与CsiNetPro相比，本策略将迫零波束成形的和速率提升了17%，模型参数数量减少了23.4倍，从而显著降低了卸载开销。同时，PCA具有轻量级无监督训练特性，所需训练样本仅为CsiNetPro的八分之一。