# Deep Learning-Based Pilotless Spatial Multiplexing

arXiv: https://arxiv.org/abs/2312.05158 · HTML: https://mlsanity.com/paper/2312.05158/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 8 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection](https://mlsanity.com/paper/2007.00140/) (2020)
- [DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations](https://mlsanity.com/paper/2010.16283/) (2020)
- [A Model-Driven Deep Learning Method for Massive MIMO Detection](https://mlsanity.com/paper/2004.06949/) (2020)

## Built on by (most cited first, 1 of 1)

- [EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver](https://mlsanity.com/paper/2602.11834/) (2026)

## Abstract (arXiv)

This paper investigates the feasibility of machine learning (ML)-based pilotless spatial multiplexing in multiple-input and multiple-output (MIMO) communication systems. Especially, it is shown that by training the transmitter and receiver jointly, the transmitter can learn such constellation shapes for the spatial streams which facilitate completely blind separation and detection by the simultaneously learned receiver. To the best of our knowledge, this is the first time ML-based spatial multiplexing without channel estimation pilots is demonstrated. The results show that the learned pilotless scheme can outperform a conventional pilot-based system by as much as 15-20% in terms of spectral efficiency, depending on the modulation order and signal-to-noise ratio.