Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers

TL;DR

This paper proposes a deep learning aided MIMO OFDM receiver that effectively mitigates nonlinear power amplifier distortions, improving bit error rate performance and robustness compared to traditional methods.

Contribution

It introduces a novel deep learning based receiver architecture that combines model aided and data driven approaches for nonlinear distortion mitigation in MIMO OFDM systems.

Findings

Superior bit error rate performance demonstrated in simulations

Robustness across various clipping distortion levels shown

Enhanced channel estimation and detection accuracy achieved

Abstract

Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems. However, due to the issue of high peak-to-average power ratio (PAPR), the OFDM symbols may suffer from nonlinear distortions of the power amplifier (PA) at the transmitters, which degrades the channel estimation and detection performances of the receivers. To mitigate the clipping distortions at the receivers end, we leverage deep learning (DL) and devise a DL based receiver which is aided by the traditional least square (LS) channel estimation and the zero-forcing (ZF) equalization models. Moreover, a data driven DL based receiver without explicit channel estimation is proposed and combined with the model aided DL based receiver to further improve the performance. Simulation results showcase that the proposed model aided DL based receiver has superior performance of bit error rate and has robustness over different levels of clipping distortions.