Intelligent Channel State Information Feedback Using Variable-Rate Autoencoder

Accurate channel state information (CSI) at the base station (BS) is crucial for achieving high beamforming gains in massive multiple-input multiple-output (MIMO) systems. In frequency division duplex (FDD) systems, the BS relies on feedback from the users (UEs) to obtain CSI. However, the aggressive CSI compression and quantization at the UE limits the achievement of the optimal beamforming gains. To resolve this problem, we propose an autoencoder network that learns a compact, discrete representation of the channel for variable feedback rates. We train and evaluate our variable-rate autoencoder using a large synthetic dataset, generated through advanced 3D modeling tools and ray-tracing simulators. Simulation results demonstrate that the proposed network achieves more accurate channel reconstruction compared to competitive methods.