Quantization Design for Deep Learning-Based CSI Feedback

Manru Yin; Shengqian Han; Chenyang Yang

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arXiv · 2503.08125

Quantization Design for Deep Learning-Based CSI Feedback

Manru Yin·Shengqian Han·Chenyang Yang

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Abstract

Deep learning-based autoencoders have been employed to compress and reconstruct channel state information (CSI) in frequency-division duplex systems. Practical implementations require judicious quantization of encoder outputs for digital transmission. In this paper, we propose a novel quantization module with bit allocation among encoder outputs and develop a method for joint training the module and the autoencoder. To enhance learning performance, we design a loss function that adaptively weights the quantization loss and the logarithm of reconstruction loss. Simulation results show the performance gain of the proposed method over existing baselines.

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BibTeX RIS

Manru Yin, Shengqian Han, Chenyang Yang. 2025-03-11. Quantization Design for Deep Learning-Based CSI Feedback. https://arxiv.org/abs/2503.08125

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