Learning Variable-Rate CSI Compression with Multi-Stage Vector Quantization
Jun-Ho Lee, Jaein Kim, Yoojin Choi · 2024
Artificial intelligence (AI) has emerged as a promising technology to design efficient channel state information (CSI) feedback methods. The effectiveness of learned CSI compression has been proven experimentally in many previous works, but less attention was paid to the importance of scalability in practice to support variable sizes of feedback payloads. In this paper, we propose employing multi-stage vector quantization (VQ) to provide progressive variable-rate CSI compression. In particular, VQ is cascaded to find a series of low-dimensional CSI representations, which provides progressively refined approximations associated with variable feedback rates. In practice, deploying a dedicated model with a separate codebook optimized for each rate requirement is prominently challenging in the aspects of storage, on-device compiling, if needed, and model life-cycle management. To tackle this challenge, we develop a single network model with multi-stage codebooks, which is learned for various compression rates of interest jointly. Moreover, we propose employing sequential training to aid joint training by providing good initialization of network parameters with transfer learning. The simulation results shows the effectiveness of the proposed model and training method, which establish a promising design methodology of AI-enabled CSI compression.