Variable-Length Finite-Rate CSI Feedback With Generative Priors

Abstract

This letter studies scalable finite-rate CSI feedback for FDD massive MIMO. Existing scalable neural schemes usually obtain rate flexibility by ordering, masking, quantizing, vector-quantizing, or entropy-coding learned latents, which couples the finite-bit interface to a task-specific latent codec. We propose CsiCoGen, a generative feedback mechanism that moves the finite-bit decision to codebook-constrained Gaussian innovation selection along a reverse diffusion trajectory. A synchronized pseudo-random Gaussian codebook makes each index a generative update instruction; a length-$L$ prefix uses $R_L=L\log_2K$ bits and yields a valid CSI estimate. The codebook is training-free and not transmitted online, while the denoiser is pretrained as a shared CSI prior. On COST2100, CsiCoGen attains indoor/outdoor NMSE of $-28.58$/$-13.96$ dB at $792$ bits and $-30.72$/$-20.37$ dB at $1592$ bits, with corresponding $ρ$ values of $0.9964$/$0.9597$ and $0.9967$/$0.9748$. Accelerated-sampling throughput and MRT spectral-efficiency results further quantify the complexity and link-level effects.