Publication Type:

Conference Paper

Source:

IEEE International conference on Wireless Communication and Networking (WiSPNET 2017), , Institute of Electrical and Electronics Engineers Inc., Volume 2018-January, Chennai, India, p.1994-1998 (2017)

ISBN:

9781509044412

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046374787&doi=10.1109%2fWiSPNET.2017.8300110&partnerID=40&md5=aacd2c69a78ad9c1a182838540271d2e

Keywords:

Behavioral model, Behavioral research, Bit error rate, Class-AB power amplifiers, Digital predistortion, Energy efficiency, Energy efficient, Energy utilization, Error performance, Inverse problems, Light amplifiers, Neural networks, Nonlinearity, Power amplifiers, RF power amplifiers, Signal processing, Wireless systems, Wireless telecommunication systems

Abstract:

<p>RF Power Amplifiers (PA) consumes a major part of available DC power in any wireless system. This article deals with behavioral modeling of RF Power Amplifiers using artificial neural networks. The developed model enables us to find energy consumption for a signal passing through the PA for a given Gain and maximum allowable distortion. The proposed modeling approach also enables us to linearize the PA by incorporating an inverse model of the PA in the baseband signal processor for compensating the distortion. The PA model can also be used as a sub-system model for evaluating the error performance of the overall system in terms of bit error rate (BER). The modeling method is validated for a class AB power amplifier design. © 2017 IEEE.</p>

Notes:

cited By 0; Conference of 2nd IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017 ; Conference Date: 22 March 2017 Through 24 March 2017; Conference Code:134757

Cite this Research Publication

R. V. S. Devi and Dr. Dhanesh G. Kurup, “Behavioral modeling of RF power amplifiers for designing energy efficient wireless systems”, in IEEE International conference on Wireless Communication and Networking (WiSPNET 2017), , Chennai, India, 2017, vol. 2018-January, pp. 1994-1998.