Attention-Infused Autoencoder for Massive MIMO CSI Compression
Kangzhi Lou, Han Ji, Xiping Steven Wu · 2025
The ever growing number of antennas in massive multiple-input multiple-output (MIMO) systems have significantly burdened the demand for the feedback of channel state information (CSI). This drives the research on CSI compression for massive MIMO. Recent development of autoencoder-based methods has proven to break the limit of conventional data compression methods in terms of both accuracy and computational complexity. However, like conventional methods, those autoencoder-based methods are trained for certain channel scenarios (such as indoor and outdoor) and would require a dedicated model for each scenario, limiting the practicability. It is challenging to develop a unified model to compress CSI across different channel scenarios, as their properties are diverse. In this paper, such a model is proposed for the first time, which is named attention-infused autoencoder network (AiANet). A dual attention mechanism is developed to capture the spatial and channel features of distinctive CSI. Multi-resolution convolutions are also employed to enhance the ability of feature extraction. Simulation results demonstrate that AiANet can substantially outperform ACRNet, which is a state-of-the-art auto encoder model. In terms of normalized mean squared error (NMSE), the proposed method can achieve an improvement of up to 3.69 dB in indoor and 1.26 dB in outdoor.