# A Model-Driven Deep Learning Method for Massive MIMO Detection

arXiv: https://arxiv.org/abs/2004.06949 · HTML: https://mlsanity.com/paper/2004.06949/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Blind Estimation of a Doubly Selective OFDM Channel: A Deep Learning Algorithm and Theory](https://mlsanity.com/paper/2206.07483/) (2022)
- [SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers](https://mlsanity.com/paper/2308.12591/) (2023)
- [Deep Learning-Based Pilotless Spatial Multiplexing](https://mlsanity.com/paper/2312.05158/) (2023)
- [Truncated Polynomial Expansion-Based Detection in Massive MIMO: A Model-Driven Deep Learning Approach](https://mlsanity.com/paper/2402.12595/) (2024)
- [ISDNN: A Deep Neural Network for Channel Estimation in Massive MIMO systems](https://mlsanity.com/paper/2410.20110/) (2024)

## Abstract (arXiv)

In this paper, an efficient massive multiple-input multiple-output (MIMO) detector is proposed by employing a deep neural network (DNN). Specifically, we first unfold an existing iterative detection algorithm into the DNN structure, such that the detection task can be implemented by deep learning (DL) approach. We then introduce two auxiliary parameters at each layer to better cancel multiuser interference (MUI). The first parameter is to generate the residual error vector while the second one is to adjust the relationship among previous layers. We further design the training procedure to optimize the auxiliary parameters with pre-processed inputs. The so derived MIMO detector falls into the category of model-driven DL. The simulation results show that the proposed MIMO detector can achieve preferable detection performance compared to the existing detectors for massive MIMO systems.