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arXiv · 2606.06846

Variable-Length Finite-Rate CSI Feedback With Generative Priors

Yangxuan Cheng·Fanyang Meng·Jian Zou·Jiacheng Xie·Zhongqiang Zhang·Ye Wang·Yongsheng Liang
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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 $\rho$ 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.

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Yangxuan Cheng, Fanyang Meng, Jian Zou, Jiacheng Xie, Zhongqiang Zhang, Ye Wang, Yongsheng Liang. 2026-06-05. Variable-Length Finite-Rate CSI Feedback With Generative Priors. https://arxiv.org/abs/2606.06846

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