# Supervised and Semi-Supervised Learning for MIMO Blind Detection with Low-Resolution ADCs

## TL;DR

This paper explores learning-based blind detection methods for MIMO systems with low-resolution ADCs, demonstrating improved accuracy and robustness without requiring explicit channel state information.

## Contribution

It introduces two novel learning approaches for blind detection in low-resolution ADC MIMO systems, utilizing CRC and data-assisted training, with analytical performance insights.

## Key findings

- Proposed methods outperform existing techniques in accuracy.
- Methods are more robust to channel distortions.
- Performance analysis guides signal design for 1-bit ADCs.

## Abstract

The use of low-resolution analog-to-digital converters (ADCs) is considered to be an effective technique to reduce the power consumption and hardware complexity of wireless transceivers. However, in systems with low-resolution ADCs, obtaining channel state information (CSI) is difficult due to significant distortions in the received signals. The primary motivation of this paper is to show that learning techniques can mitigate the impact of CSI unavailability. We study the blind detection problem in multiple-input-multiple-output (MIMO) systems with low-resolution ADCs using learning approaches. Two methods, which employ a sequence of pilot symbol vectors as the initial training data, are proposed. The first method exploits the use of a cyclic redundancy check (CRC) to obtain more training data, which helps improve the detection accuracy. The second method is based on the perspective that the to-be-decoded data can itself assist the learning process, so no further training information is required except the pilot sequence. For the case of 1-bit ADCs, we provide a performance analysis of the vector error rate for the proposed methods. Based on the analytical results, a criterion for designing transmitted signals is also presented. Simulation results show that the proposed methods outperform existing techniques and are also more robust.