# Model-Driven Deep Learning for MIMO Detection

## TL;DR

This paper introduces a model-driven deep learning approach for MIMO detection that unfolds an iterative algorithm with trainable parameters, enabling efficient training, improved performance, and robustness over traditional and other DL-based detectors.

## Contribution

It proposes a novel model-driven DL MIMO detector based on unfolding iterative algorithms, with fewer parameters and enhanced robustness, also extending to joint channel estimation and detection.

## Key findings

- Significantly outperforms traditional iterative detectors.
- Outperforms other deep learning-based MIMO detectors.
- Exhibits superior robustness to mismatches.

## Abstract

In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft-input soft-output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection (JCESD), where the detector takes channel estimation error and channel statistics into consideration while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based MIMO detectors and exhibits superior robustness to various mismatches.