Learning Based CSI Look Up Table: A Novel Vector Quantization Approach for High Accuracy CSI Reconstruction

To fully exploit the benefits of spatial multiplexing gains within Frequency Division Duplex (FDD) Multiple-Input Multiple-Output (MIMO) systems, the development of a robust Channel State Information (CSI) feedback compression methodology with low over- the-air CSI overhead while achieving high reconstruction accuracy is critical. Such methodology must be able to effectively reduce communication overhead without compromising system level performance. Traditional codebook-based approach, as outlined in the 3rd Generation Partnership Project (3GPP) standards encounters significant challenges to balance air-interface overhead and computational complexity. In this paper, we introduce a novel and adaptable Deep Leaning (DL) based CSI codebook technique leveraging vector quantization known as the CSI-Look-Up Table (CSI-LUT). Our numerical results show that the CSI - L UT has potential in reducing > 90 % of CSI overhead for max rank 1 while still achieving better average reconstruction accuracy and system level performance than traditional codebook-based approach. We anticipate that such advancements will play a pivotal role in enhancing the efficiency and performance of CSI feedback in MIMO systems, contributing significantly to the evolving landscape of 5G and beyond.