2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSPCOM-8.3
Paper Title MITIGATING CLIPPING DISTORTION IN OFDM USING DEEP RESIDUAL LEARNING
Authors Muhammad Shahmeer Omar, Xiaoli Ma, Georgia Institute of Technology, United States
SessionSPCOM-8: Deep learning for communications
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Signal Processing for Communications and Networking: [SPC-ML] Machine Learning for Communications
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Virtual Presentation  Click here to watch in the Virtual Conference
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.