Codebook-Agnostic Separate Training for DL-based CSI Feedback Enhancement

CSI compression, which serves as the first-tire use case for AI/ML applications in 3GPP, has gained widespread attention. The 3GPP-defined Type-3 training with CSI encoder and decoder sequentially trained in different sessions has been accorded a prioritized option due to its exceptional capacity for preserving intellectual property. However, current framework incorporating strong constraints on the latent feature, compromises the principle of separate training. In addition, in the context of a single UE served by multiple NW vendors, the adoption of a unified UE encoder accommodating multiple NW vendors is compelling from complexity perspective, yet remains unexplored. This paper proposes a novel separate training framework followed by a UE-side model training scheme, which leverages the vector quantizer (VQ) with a vendor-proprietary codebook to conceal the interpretation between disclosed codewords and latent representations. We further extend this scheme to encompass multi-NW-vendor scenarios, which enables a unified encoder compatible with multiple NW decoders. Simulation results on the system level simulation dataset and real-world over-the-air dataset demonstrate that sequentially trained encoder-decoder can cooperate seamlessly with negligible performance degradation compared to the Type-1 encoder-decoder joint training. Furthermore, the proposed scheme remains insensitive to changes in the number of NW vendors and codebook variations.