# GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access

arXiv: https://arxiv.org/abs/2206.05998 · HTML: https://mlsanity.com/paper/2206.05998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Real-time Neural Radiance Caching for Path Tracing](https://mlsanity.com/paper/2106.12372/) (2021)
- [Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges](https://mlsanity.com/paper/1609.06261/) (2016)
- [Non-Orthogonal Multiple Access (NOMA) in Cellular Uplink and Downlink: Challenges and Enabling Techniques](https://mlsanity.com/paper/1608.05783/) (2016)
- [Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform](https://mlsanity.com/paper/1911.04291/) (2019)
- [GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems](https://mlsanity.com/paper/2201.05024/) (2022)

## Abstract (arXiv)

Non-orthogonal multiple access (NOMA) is an interesting technology that enables massive connectivity as required in future 5G and 6G networks. While purely linear processing already achieves good performance in NOMA systems, in certain scenarios, non-linear processing is mandatory to ensure acceptable performance. In this paper, we propose a neural network architecture that combines the advantages of both linear and non-linear processing. Its real-time detection performance is demonstrated by a highly efficient implementation on a graphics processing unit (GPU). Using real measurements in a laboratory environment, we show the superiority of our approach over conventional methods.