# Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications

arXiv: https://arxiv.org/abs/2301.00516 · HTML: https://mlsanity.com/paper/2301.00516/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 17 of 17)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures](https://mlsanity.com/paper/1409.2574/) (2014)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [On weight initialization in deep neural networks](https://mlsanity.com/paper/1704.08863/) (2017)
- [Learned D-AMP: Principled Neural Network based Compressive Image Recovery](https://mlsanity.com/paper/1704.06625/) (2017)
- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [Massive Access for 5G and Beyond](https://mlsanity.com/paper/2002.03491/) (2020)
- [ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers](https://mlsanity.com/paper/1810.09082/) (2018)
- [Grant-free Non-orthogonal Multiple Access for IoT: A Survey](https://mlsanity.com/paper/1910.06529/) (2019)
- [Massive Machine-type Communications in 5G: Physical and MAC-layer solutions](https://mlsanity.com/paper/1606.03893/) (2016)
- [Fast mmwave Beam Alignment via Correlated Bandit Learning](https://mlsanity.com/paper/1909.03313/) (2019)
- [Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems](https://mlsanity.com/paper/2002.08710/) (2020)
- [A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems](https://mlsanity.com/paper/1604.04248/) (2016)
- [White Paper on Critical and Massive Machine Type Communication Towards 6G](https://mlsanity.com/paper/2004.14146/) (2020)
- [Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access](https://mlsanity.com/paper/1807.10911/) (2018)
- [End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels](https://mlsanity.com/paper/2105.01260/) (2021)

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

- [Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems](https://mlsanity.com/paper/2502.13390/) (2025)
- [Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication](https://mlsanity.com/paper/2403.07255/) (2024)
- [The Blind Normalized Stein Variational Gradient Descent-Based Detection for Intelligent Random Access in Cellular IoT](https://mlsanity.com/paper/2403.18846/) (2024)

## Abstract (arXiv)

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the non-coherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance.