# Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication

arXiv: https://arxiv.org/abs/2403.07255 · HTML: https://mlsanity.com/paper/2403.07255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [Massive Access for 5G and Beyond](https://mlsanity.com/paper/2002.03491/) (2020)
- [Grant-free Non-orthogonal Multiple Access for IoT: A Survey](https://mlsanity.com/paper/1910.06529/) (2019)
- [Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO](https://mlsanity.com/paper/2111.15378/) (2021)
- [Deep Neural Network Based Active User Detection for Grant-free NOMA Systems](https://mlsanity.com/paper/1912.11782/) (2019)
- [Towards Enabling Critical mMTC: A Review of URLLC within mMTC](https://mlsanity.com/paper/2007.08793/) (2020)
- [NOMA for Next-generation Massive IoT: Performance Potential and Technology Directions](https://mlsanity.com/paper/2104.04911/) (2021)
- [Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access](https://mlsanity.com/paper/2107.05246/) (2021)
- [Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications](https://mlsanity.com/paper/2301.00516/) (2023)
- [DNN-Aided Block Sparse Bayesian Learning for User Activity Detection and Channel Estimation in Grant-Free Non-Orthogonal Random Access](https://mlsanity.com/paper/1910.02953/) (2019)
- [Supporting More Active Users for Massive Access via Data-assisted Activity Detection](https://mlsanity.com/paper/2102.08621/) (2021)
- [Activity Detection for Grant-Free NOMA in Massive IoT Networks](https://mlsanity.com/paper/2301.01274/) (2022)

## Abstract (arXiv)

In this paper, we present a novel approach for joint activity detection (AD), channel estimation (CE), and data detection (DD) in uplink grant-free non-orthogonal multiple access (NOMA) systems. Our approach employs an iterative and parallel interference removal strategy inspired by parallel interference cancellation (PIC), enhanced with deep learning to jointly tackle the AD, CE, and DD problems. Based on this approach, we develop three PIC frameworks, each of which is designed for either coherent or non-coherence schemes. The first framework performs joint AD and CE using received pilot signals in the coherent scheme. Building upon this framework, the second framework utilizes both the received pilot and data signals for CE, further enhancing the performances of AD, CE, and DD in the coherent scheme. The third framework is designed to accommodate the non-coherent scheme involving a small number of data bits, which simultaneously performs AD and DD. Through joint loss functions and interference cancellation modules, our approach supports end-to-end training, contributing to enhanced performances of AD, CE, and DD for both coherent and non-coherent schemes. Simulation results demonstrate the superiority of our approach over traditional techniques, exhibiting enhanced performances of AD, CE, and DD while maintaining lower computational complexity.