# Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers

arXiv: https://arxiv.org/abs/2506.20248 · HTML: https://mlsanity.com/paper/2506.20248/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2025.

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

- [Optimal Power Allocation and AI Receiver Design for Superimposed DMRS and Data Transmission](https://mlsanity.com/paper/2608.13809/) (2026)

## Abstract (arXiv)

Fifth-generation (5G) systems utilize orthogonal demodulation reference signals (DMRS) to enable channel estimation at the receiver. These orthogonal DMRS-also referred to as pilots-are effective in avoiding pilot contamination and interference from both the user's own data and that of others. However, this approach incurs a significant overhead, as a substantial portion of the time-frequency resources must be reserved for pilot transmission. Moreover, the overhead increases with the number of users and transmission layers. To address these limitations in the context of emerging sixth-generation (6G) systems and to support data transmission across the entire time-frequency grid, the superposition of data and DMRS symbols has been explored as an alternative DMRS transmission strategy. In this study, we propose an enhanced version of DeepRx, a deep convolutional neural network (CNN)-based receiver, capable of estimating the channel from received superimposed (SI) DMRS symbols and reliably detecting the transmitted data. We also design a conventional receiver for comparison, which estimates the channel from SI DMRS using classical signal processing techniques. Extensive evaluations in both uplink single-user and multi-user scenarios demonstrate that DeepRx consistently outperforms the conventional receivers in terms of performance.