Mitigating Clipping Distortion in Multicarrier Transmissions Using Tensor-Train Deep Neural Networks

Muhammad Shahmeer Omar*, Jun Qi, Xiaoli Ma

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

3 Citations (Scopus)


Multicarrier transmissions, such as orthogonal frequency/chirp division multiplexing (OF/CDM), offer high spectral efficiency and low complexity equalization in multipath fading channels at the cost of high peak-to-average power ratio (PAPR). High peak powers can occur randomly and may drive the power amplifier (PA) into saturation, resulting in non-linear distortion. In this work, we propose a novel tensor-train (TT) deep neural network (DNN) architecture combined with soft clipping to reduce the PAPR to a deterministic level and minimize in-band distortion, while also satisfying spectral mask constraints. The proposed solution requires modifications only at the transmitter and there is no loss of spectral efficiency. Employing the TT decomposition allows for significant reduction in parameters. Results show that the proposed solution allows for significant reduction in PAPR with minimal performance loss. Furthermore, an upper bound for the PAPR is also derived which allows for the prediction of the required input power back off (IBO) without extensive simulations and trial-and-error.

Original languageEnglish
Pages (from-to)2127-2138
Number of pages12
JournalIEEE Transactions on Wireless Communications
Issue number3
Early online date3 Oct 2022
Publication statusPublished - Mar 2023

Scopus Subject Areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

User-Defined Keywords

  • Tensor-train networks
  • neural networks
  • peak-to-average power ratio
  • OCDM
  • OFDM


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