Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder

TL;DR

This paper introduces a novel convolutional autoencoder-based framework for reducing PAPR and designing waveforms in MIMO-OFDM systems, optimizing performance without side information and handling complex channel effects.

Contribution

It presents a joint learning scheme using a convolutional autoencoder for PAPR reduction, spectrum shaping, and MIMO detection, all within a single model for the first time.

Findings

Achieves competitive BER and PAPR reduction compared to classical methods.

Utilizes a single PAPR reduction block for all antennas, simplifying implementation.

Demonstrates effective multi-objective optimization across various SNR levels.

Abstract

An enhanced framework for peak-to-average power ratio ($\mathsf{PAPR}$) reduction and waveform design for Multiple-Input-Multiple-Output ($\mathsf{MIMO}$) orthogonal frequency-division multiplexing ($\mathsf{OFDM}$) systems, based on a convolutional-autoencoder ($\mathsf{CAE}$) architecture, is presented. The end-to-end learning-based autoencoder ($\mathsf{AE}$) for communication networks represents the network by an encoder and decoder, where in between, the learned latent representation goes through a physical communication channel. We introduce a joint learning scheme based on projected gradient descent iteration to optimize the spectral mask behavior and MIMO detection under the influence of a non-linear high power amplifier ($\mathsf{HPA}$) and a multipath fading channel. The offered efficient implementation novel waveform design technique utilizes only a single $\mathsf{PAPR}$ reduction block for all antennas. It is throughput-lossless, as no side information is required at the decoder. Performance is analyzed by examining the bit error rate ($\mathsf{BER}$), the $\mathsf{PAPR}$, and the spectral response and compared with classical $\mathsf{PAPR}$ reduction $\mathsf{MIMO}$ detector methods on 5G simulated data. The suggested system exhibits competitive performance when considering all optimization criteria simultaneously. We apply gradual loss learning for multi-objective optimization and show empirically that a single trained model covers the tasks of $\mathsf{PAPR}$ reduction, spectrum design, and $\mathsf{MIMO}$ detection together over a wide range of SNR levels.