Unsupervised Learning for Reference Signals Overhead Reduction in 3GPP MIMO Systems

Abstract

The unprecedented increase in the number of wireless-connected devices requires novel solutions to improve the data rate at low latency. Reference signals overhead reduction is a powerful way to increase the data rates. However, excessive reduction in the number of reference signals degrades the channel estimation performance with potential negative impacts on the data rates. Toward this end, this paper proposes a machine learning-based approach that enables reference signal-free data channel demodulation. This new approach involves a repetition of part of the data channel symbols across the slot. Invoking canonical correlation analysis on the repeated data at the user side yields high-quality combiners that are used to recover both the repeated data blocks and the rest of the data symbols in the slot, without the need of traditional channel estimation. This paper also proposes two effective and principled strategies; one for repetition pattern selection as a function of the channel parameters and the other addresses performance in highly frequency selective channels. The proposed approach offers considerable gains in throughput performance and complexity reduction. Simulation results using a 3GPP NR link-level test bench, reveal the effectiveness of the proposed approach relative to the state-of-the-art methods.

Keywords

• Canonical correlation analysis
• machine learning (ML)
• unsupervised learning
• 3GPP
• 5G NR
• identifiability