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

TL;DR

This paper proposes a deep learning-based receiver for superimposed DMRS in 6G uplink OFDM, outperforming traditional methods in channel estimation and data detection, thus reducing pilot overhead.

Contribution

It introduces an enhanced DeepRx CNN-based receiver for superimposed DMRS, enabling efficient channel estimation and data detection in 6G uplink scenarios.

Findings

DeepRx outperforms classical receivers in various scenarios.

Superimposed DMRS reduces pilot overhead in 6G systems.

Enhanced neural network improves channel estimation accuracy.

Abstract

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.