Work

Machine Learning Physical-Layer Receivers for DFT-s-OFDM

Year: 2025

Type: article

Abstract: In this work, we propose two alternative machine learning (ML) based physical-layer receivers for discrete Fourier transform (DFT) precoded multicarrier systems. The receivers are designed to provide reliable soft bit estimates under two coexisting transmitter impairments, namely, power amplifier nonlinear distortion and oscillator phase noise. Considering data modulations up to 256-QAM, realistic 5G NR uplink evaluations at 28 GHz demonstrate large performance gains compared to reference receivers, especially when the ML receiver has trainable layers both before and after the inverse DFT. Furthermore, compared to a baseline linear receiver, we show an uplink coverage increase of over 20% with the proposed fully learned receiver.

Source: IEEE Transactions on Vehicular Technology

DOI: 10.1109/tvt.2025.3556843

Authors: Jaakko Pihlajasalo, Dani Korpi, Taneli Riihonen, Jukka Talvitie, Mikko Valkama

Institutions: Tampere University, University of Tampere, Nokia (Finland)

Language: English

Topic: PAPR reduction in OFDM

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funder: Academy of Finland