Low Complexity Neural Network Based Digital Predistortion for Memory Power Amplifier
| Autor: | Hmaied Shaiek, Yassine Bendimerad, Daniel Roviras, Meryem M. Benosman, Fethi Tarik Bendimerad, Rafik Zayani |
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| Přispěvatelé: | CEDRIC. Traitement du signal et architectures électroniques (CEDRIC - LAETITIA), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Université de Tlemcen, Bouzefrane S., Laurent M., Boumerdassi S., Renault E. (eds) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mean squared error
Artificial neural network Computer science Amplifier 020206 networking & telecommunications 02 engineering and technology Predistortion Reduction (complexity) Multilayer perceptron Distortion Out-of-band management 0202 electrical engineering electronic engineering information engineering Electronic engineering 020201 artificial intelligence & image processing [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Mobile, Secure, and Programmable Networking. MSPN 2020 The 6th International Conference on Mobile, Secure and Programmable Networking The 6th International Conference on Mobile, Secure and Programmable Networking, Oct 2020, Paris, France. ⟨10.1007/978-3-030-67550-9_16⟩ Mobile, Secure, and Programmable Networking ISBN: 9783030675493 MSPN |
| DOI: | 10.1007/978-3-030-67550-9_16⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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