# A Low-Complexity ADMM-based Massive MIMO Detectors via Deep Neural Networks

arXiv: https://arxiv.org/abs/2103.00131 · HTML: https://mlsanity.com/paper/2103.00131/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2021.

## Built on by (most cited first, 1 of 1)

- [Edge Graph Neural Networks for Massive MIMO Detection](https://mlsanity.com/paper/2206.06979/) (2022)

## Abstract (arXiv)

An alternate direction method of multipliers (ADMM)-based detectors can achieve good performance in both small and large-scale multiple-input multiple-output (MIMO) systems. However, due to the difficulty of choosing the optimal penalty parameters, their performance is limited. This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation. It exploits the unfolding technique and learns to estimate the penalty parameters. Additionally, a computationally cheaper detector is also proposed. The proposed methods can handle the higher-order modulation signals. Numerical results are presented to demonstrate the performances of the proposed methods compared with the existing works.