Abstract
Digital Predistortion (DPD) is an effective technique for Power Amplifier (PA) non-linear distortion and memory effects compensation. Different topoligies of DPD are presented in the literature. In this paper, we propose a mimetic neural network based DPD for Hammerstein power amplifier for OFDM signal with a reduction of Peak to Average Power Ration (PAPR) by Selective Mapping (SLM) method. This proposed model is compared with Real Valued Multilayer Perceptron (R-MLP). Simulation results show that the mimetic-R-MLP manifests more efficiency for PA linearization and for memory effect reduction in terms of Error Vector Magnitude (EVM) by a gain of 2 dB. It outperforms the R-MLP in terms of Mean Squared Error (MSE) for the convergence of the Neural Network (NN) and its complexity is \(23\%\) lower. The results in terms of Power Spectral Density (DSP) show also that our model compensates efficiently the out of band distortion (OOB) of the PA.
This work is supported by the Patenariat Huber Curien (PHC) Tasilli project: ATOME5+ 19MDU2014.
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Benosman, M.M., Shaiek, H., Bendimerad, Y., Zayani, R., Roviras, D., Bendimerad, F.T. (2021). Low Complexity Neural Network Based Digital Predistortion for Memory Power Amplifiers. In: Bouzefrane, S., Laurent, M., Boumerdassi, S., Renault, E. (eds) Mobile, Secure, and Programmable Networking. MSPN 2020. Lecture Notes in Computer Science(), vol 12605. Springer, Cham. https://doi.org/10.1007/978-3-030-67550-9_16
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