Unsupervised Learning for Pilot-free Transmission in 3GPP MIMO Systems

TL;DR

This paper proposes a novel pilot-free downlink data transmission method for 3GPP MIMO systems that leverages data repetition and canonical correlation analysis to eliminate the need for channel estimation, improving spectral efficiency.

Contribution

It introduces a new data structure that removes the need for demodulation reference signals and develops mechanisms to enhance CCA performance in OFDM systems.

Findings

Outperforms state-of-the-art methods in simulations

Reduces reference signal overhead significantly

Maintains reliable data recovery without channel estimation

Abstract

Reference signals overhead reduction has recently evolved as an effective solution for improving the system spectral efficiency. This paper introduces a new downlink data structure that is free from demodulation reference signals (DM-RS), and hence does not require any channel estimation at the receiver. The new proposed data transmission structure involves a simple repetition step of part of the user data across the different sub-bands. Exploiting the repetition structure at the user side, it is shown that reliable recovery is possible via canonical correlation analysis. This paper also proposes two effective mechanisms for boosting the CCA performance in OFDM systems; one for repetition pattern selection and another to deal with the severe frequency selectivity issues. The proposed approach exhibits favorable complexity-performance tradeoff, rendering it appealing for practical implementation. Numerical results, using a 3GPP link-level testbench, demonstrate the superiority of the proposed approach relative to the state-of-the-art methods.