CSI Compression Beyond Latents: End-to-End Hybrid Attention-CNN Networks with Entropy Regularization (2509.08776v1)

Abstract: Massive MIMO systems rely on accurate Channel State Information (CSI) feedback to enable high-gain beam- forming. However, the feedback overhead scales linearly with the number of antennas, presenting a major bottleneck. While recent deep learning methods have improved CSI compres- sion, most overlook the impact of quantization and entropy coding, limiting their practical deployability. In this work, we propose an end-to-end CSI compression framework that inte- grates a Spatial Correlation-Guided Attention Mechanism with quantization and entropy-aware training. Our model effectively exploits the spatial correlation among the antennas, thereby learning compact, entropy-optimized latent representations for efficient coding. This reduces the required feedback bitrates without sacrificing reconstruction accuracy, thereby yielding a superior rate-distortion trade-off. Experiments show that our method surpasses existing end-to-end CSI compression schemes, exceeding benchmark performance by an average of 21.5% on indoor datasets and 18.9% on outdoor datasets. The proposed framework results in a practical and efficient CSI feedback scheme.