# Adaptive Learning-Based Detection for One-Bit Quantized Massive MIMO Systems

arXiv: https://arxiv.org/abs/2211.06995 · HTML: https://mlsanity.com/paper/2211.06995/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [What Will 5G Be?](https://mlsanity.com/paper/1405.2957/) (2014)
- [SVM-based Channel Estimation and Data Detection for One-Bit Massive MIMO Systems](https://mlsanity.com/paper/2003.10678/) (2020)
- [Quantized Massive MIMO Systems with Multicell Coordinated Beamforming and Power Control](https://mlsanity.com/paper/2005.07853/) (2020)
- [Construction of 1-Bit Transmit Signal Vectors for Downlink MU-MISO Systems: QAM constellations](https://mlsanity.com/paper/2005.07879/) (2020)

## Abstract (arXiv)

We propose an adaptive learning-based framework for uplink massive multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital converters. Learning-based detection does not need to estimate channels, which overcomes a key drawback in one-bit quantized systems. During training, learning-based detection suffers at high signal-to-noise ratio (SNR) because observations will be biased to +1 or -1 which leads to many zero-valued empirical likelihood functions. At low SNR, observations vary frequently in value but the high noise power makes capturing the effect of the channel difficult. To address these drawbacks, we propose an adaptive dithering-and-learning method. During training, received values are mixed with dithering noise whose statistics are known to the base station, and the dithering noise power is updated for each antenna element depending on the observed pattern of the output. We then use the refined probabilities in the one-bit maximum likelihood detection rule. Simulation results validate the detection performance of the proposed method vs. our previous method using fixed dithering noise power as well as zero-forcing and optimal ML detection both of which assume perfect channel knowledge.