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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, ispresented. The end-to-end learning-based autoencoder ($\mathsf{AE}$) forcommunication networks represents the network by an encoder and decoder, wherein between, the learned latent representation goes through a physicalcommunication channel. We introduce a joint learning scheme based on projectedgradient descent iteration to optimize the spectral mask behavior and MIMOdetection under the influence of a non-linear high power amplifier($\mathsf{HPA}$) and a multipath fading channel. The offered efficientimplementation novel waveform design technique utilizes only a single$\mathsf{PAPR}$ reduction block for all antennas. It is throughput-lossless, asno side information is required at the decoder. Performance is analyzed byexamining the bit error rate ($\mathsf{BER}$), the $\mathsf{PAPR}$, and thespectral response and compared with classical $\mathsf{PAPR}$ reduction$\mathsf{MIMO}$ detector methods on 5G simulated data. The suggested systemexhibits competitive performance when considering all optimization criteriasimultaneously. We apply gradual loss learning for multi-objective optimizationand show empirically that a single trained model covers the tasks of$\mathsf{PAPR}$ reduction, spectrum design, and $\mathsf{MIMO}$ detectiontogether over a wide range of SNR levels.

Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder