SNAP–CSI: Personalized Neural Compression for Enhanced CSI Compression in Wireless Networks

This paper introduces the Selection Network Assisted Personalized CSI Compression (SNAP–CSI) algorithm, a novel approach for efficient Channel State Information (CSI) compression in wireless networks. Focusing on scenarios where CSI from User Equipment (UE) is transmitted to a Base Station (BS) via a rate-limited channel, SNAP–CSI employs Deep Neural Networks (DNNs) trained on historical CSI data for enhanced compression. Central to SNAP–CSI is the exploitation of CSI heterogeneity to cluster users, enabling the training of tailored personalized models. These models comprise an encoder at the UE and a decoder at the BS, optimized for efficient compression with minimal parameters, specific to each user cluster. A key innovation in SNAP–CSI is the development of a Selection Network (SN). This network predicts cluster membership from compressed CSI data, allowing UEs to select the most fitting personalized model for the lowest data distortion. Concurrently, the BS utilizes the SN for accurate reconstruction of compressed CSI, thus negating the need for further synchronization. The effectiveness of SNAP–CSI is validated through simulations with the ultra-dense indoor maMIMO dataset [2], evaluating performance across diverse heterogeneity conditions and UE-to-BS channel rates.