학술논문

Mitigating Clipping Distortion in OFDM Using Deep Residual Learning
Document Type
Conference
Source
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2021 - 2021 IEEE International Conference on. :4925-4929 Jun, 2021
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Training
Acoustic distortion
Conferences
Nonlinear distortion
Neural networks
Power amplifiers
Peak to average power ratio
OFDM
Peak-to-average power ratio
Residual neural network
Deep learning
Language
ISSN
2379-190X
Abstract
The high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) transmissions is susceptible to non-linear distortion caused by power amplifier (PA) saturation. In this work, we propose a novel technique, using residual neural networks and soft clipping, to deterministically limit the peak amplitude of the signal, thus lowering its PAPR and circumventing PA distortion. We show that the proposed solution is capable of significantly reducing PAPR and in-band distortion, while obeying a spectral mask. Furthermore, we show that the neural network is able to generalize for a range of peak amplitudes, thus eliminating the need to re-train the network when the requirement changes.