# Deep Learning Based Fast Multiuser Detection for Massive Machine-Type Communication

arXiv: https://arxiv.org/abs/1807.00967 · HTML: https://mlsanity.com/paper/1807.00967/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO](https://mlsanity.com/paper/2406.07160/) (2024)
- [Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments](https://mlsanity.com/paper/2503.10280/) (2025)
- [Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2503.11305/) (2025)

## Abstract (arXiv)

Massive machine-type communication (MTC) with sporadically transmitted small packets and low data rate requires new designs on the PHY and MAC layer with light transmission overhead. Compressive sensing based multiuser detection (CS-MUD) is designed to detect active users through random access with low overhead by exploiting sparsity, i.e., the nature of sporadic transmissions in MTC. However, the high computational complexity of conventional sparse reconstruction algorithms prohibits the implementation of CS-MUD in real communication systems. To overcome this drawback, in this paper, we propose a fast Deep learning based approach for CS-MUD in massive MTC systems. In particular, a novel block restrictive activation nonlinear unit, is proposed to capture the block sparse structure in wide-band wireless communication systems (or multi-antenna systems). Our simulation results show that the proposed approach outperforms various existing algorithms for CS-MUD and allows for ten-fold decrease of the computing time.