# Blind-Adaptive Quantizers

arXiv: https://arxiv.org/abs/2409.04077 · HTML: https://mlsanity.com/paper/2409.04077/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [On Unlimited Sampling and Reconstruction](https://mlsanity.com/paper/1905.03901/) (2019)
- [Unlimited Sampling from Theory to Practice: Fourier-Prony Recovery and Prototype ADC](https://mlsanity.com/paper/2105.05818/) (2021)
- [Above the Nyquist Rate, Modulo Folding Does Not Hurt](https://mlsanity.com/paper/1903.12289/) (2019)
- [Blind Modulo Analog-to-Digital Conversion](https://mlsanity.com/paper/2108.08937/) (2021)
- [Blind Unwrapping of Modulo Reduced Gaussian Vectors: Recovering MSBs from LSBs](https://mlsanity.com/paper/1901.10396/) (2019)
- [Robust Unlimited Sampling Beyond Modulo](https://mlsanity.com/paper/2206.14656/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fold First, Detect Directly: Communication Symbol Detection Without Unfolding for Low-Bitrate Modulo-ADCs](https://mlsanity.com/paper/2609.11298/) (2026)

## Abstract (arXiv)

Sampling and quantization are crucial in digital signal processing, but quantization introduces errors, particularly due to distribution mismatch between input signals and quantizers. Existing methods to reduce this error require precise knowledge of the input's distribution, which is often unavailable. To address this, we propose a blind and adaptive method that minimizes distribution mismatch without prior knowledge of the input distribution. Our approach uses a nonlinear transformation with amplification and modulo-folding, followed by a uniform quantizer. Theoretical analysis shows that sufficient amplification makes the output distribution of modulo-folding nearly uniform, reducing mismatch across various distributions, including Gaussian, exponential, and uniform. To recover the true quantized samples, we suggest using existing unfolding techniques, which, despite requiring significant oversampling, effectively reduce mismatch and quantization error, offering a favorable trade-off similar to predictive coding strategies.