# Deep Learning-Aided Spatial Multiplexing with Index Modulation

arXiv: https://arxiv.org/abs/2202.02856 · HTML: https://mlsanity.com/paper/2202.02856/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 6 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions](https://mlsanity.com/paper/1904.09673/) (2019)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [Multilevel MIMO Detection with Deep Learning](https://mlsanity.com/paper/1812.01571/) (2018)

## Abstract (arXiv)

In this paper, deep learning (DL)-aided data detection of spatial multiplexing (SMX) multiple-input multiple-output (MIMO) transmission with index modulation (IM) (Deep-SMX-IM) has been proposed. Deep-SMX-IM has been constructed by combining a zero-forcing (ZF) detector and DL technique. The proposed method uses the significant advantages of DL techniques to learn transmission characteristics of the frequency and spatial domains. Furthermore, thanks to using subblockbased detection provided by IM, Deep-SMX-IM is a straightforward method, which eventually reveals reduced complexity. It has been shown that Deep-SMX-IM has significant error performance gains compared to ZF detector without increasing computational complexity for different system configurations.