Wideband and Entropy-Aware Deep Soft Bit Quantization

• URL: http://arxiv.org/abs/2110.09541v1
• Date: Mon, 18 Oct 2021 18:00:05 GMT
• Title: Wideband and Entropy-Aware Deep Soft Bit Quantization
• Authors: Marius Arvinte, Jonathan I. Tamir
• Abstract summary: We introduce a novel deep learning solution for soft bit quantization across wideband channels. Our method is trained end-to-end with quantization- and entropy-aware augmentations to the loss function. Our method achieves a compression gain of up to $10 %$ in the high SNR regime versus previous state-of-the-art methods.
• Score: 1.7259824817932292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has been recently applied to physical layer processing in digital communication systems in order to improve end-to-end performance. In this work, we introduce a novel deep learning solution for soft bit quantization across wideband channels. Our method is trained end-to-end with quantization- and entropy-aware augmentations to the loss function and is used at inference in conjunction with source coding to achieve near-optimal compression gains over wideband channels. To efficiently train our method, we prove and verify that a fixed feature space quantization scheme is sufficient for efficient learning. When tested on channel distributions never seen during training, the proposed method achieves a compression gain of up to $10 \%$ in the high SNR regime versus previous state-of-the-art methods. To encourage reproducible research, our implementation is publicly available at https://github.com/utcsilab/wideband-llr-deep.

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