# Quantized Signal Recovery with Interference via Parametrized Look-Up Tables

arXiv: https://arxiv.org/abs/2507.18370 · HTML: https://mlsanity.com/paper/2507.18370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [The Cramer-Rao Bound for Signal Parameter Estimation from Quantized Data](https://mlsanity.com/paper/2209.13182/) (2022)
- [Quantized Spectral Compressed Sensing: Cramer-Rao Bounds and Recovery Algorithms](https://mlsanity.com/paper/1710.03654/) (2017)
- [Low-Resolution Quantization in Phase Modulated Systems: Optimum Detectors and Error Rate Analysis](https://mlsanity.com/paper/1902.10896/) (2019)
- [Linear and Nonlinear MMSE Estimation in One-Bit Quantized Systems under a Gaussian Mixture Prior](https://mlsanity.com/paper/2407.01305/) (2024)
- [Optimality of the Bussgang Linear MMSE Channel Estimator for MIMO Systems with 1-Bit ADCs](https://mlsanity.com/paper/2407.13921/) (2024)

## Abstract (arXiv)

Efficient all-digital post-correction of low-resolution analog-to-digital converters can be achieved by using Look-Up Tables (LUTs). The performance of a LUT can be optimized by incorporating a parametric model for the expected input signal, noise level, and interference signals. We evaluate three analytical estimators for integration with parametrized LUTs, especially with applications to low-resolution, non-linear, or wideband quantizers. We also propose several approximations to improve tractability of the estimation problem for Phase-Shift Keyed input signals and Linear Frequency Modulated interference signals. Simulated results validate the ability of our estimator to recover the instantaneous value of the desired input signal in real-time with a high degree of accuracy. This includes cancellation of harmonic distortion that aliases into the desired signal bandwidth from front-end saturation due to high-power out-of-band interference. Our estimators are shown to achieve a significant gain over conventional linear-filtering techniques while also being robust to changes in input parameters, non-linear quantizers, and time-variant interference sources. For a tone input quantized to 3 bits and estimated with a fixed 12-tap model order we achieve >10 dB improvement in Mean Square Error and >20 dBc improvement in Spurious-Free Dynamic Range.