Quantization Design for Deep Learning-Based CSI Feedback
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866917951205015552 |
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| author | Yin, Manru Han, Shengqian Yang, Chenyang |
| author_facet | Yin, Manru Han, Shengqian Yang, Chenyang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08125 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantization Design for Deep Learning-Based CSI Feedback Yin, Manru Han, Shengqian Yang, Chenyang Signal Processing 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. |
| title | Quantization Design for Deep Learning-Based CSI Feedback |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.08125 |