A Low-Complexity DPD to Fully Linearize the Power Amplifiers in a mMIMO Transmitter

A radio frequency (RF) power amplifier (PA) is crucial to enhance the signal to transmit via antenna over long distances. High-power transmission often leads to nonlinear behavior in the PA, necessitating the use of digital predistortion (DPD) signal processing to restore linearity by preinverting t...

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Vydáno v:ICC 2024 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS s. 545 - 550
Hlavní autoři: Prasad, Ganesh, Johansson, Hakan, Laskar, Rabul Hussain
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 09.06.2024
Edice:IEEE International Conference on Communications
Témata:
Digital predistortion signal processing
indirect learning architecture
Linearity
Massive MIMO
Radio frequency
Radio transmitters
recursive prediction error method
Signal processing
Signal processing algorithms
Transmitting antennas
indirect learning architecture
recursive prediction error method
Digital predistortion signal processing
ISBN:9781728190556, 172819055X, 9781728190549, 1728190541
ISSN:1938-1883
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Shrnutí:A radio frequency (RF) power amplifier (PA) is crucial to enhance the signal to transmit via antenna over long distances. High-power transmission often leads to nonlinear behavior in the PA, necessitating the use of digital predistortion (DPD) signal processing to restore linearity by preinverting the nonlinearity. However, when dealing with a massive MIMO (mMIMO) transmitter with numerous PAs, a single DPD is not enough, and allocating a separate DPD for each PA is intricate and cost-inefficient. In this study, we tackle these challenges through our proposed low-complexity DPD (LC-DPD) architecture. The LC-DPD has the flexibility to choose the parameters of its architecture as per the desired tradeoff between the performance and complexity in the linearization. It employs learning of its coefficients through algorithms utilizing an indirect learning architecture based recursive prediction error method (ILA-RPEM), which is adaptive and free from matrix inversions.
ISBN:9781728190556
172819055X
9781728190549
1728190541
ISSN:1938-1883
DOI:10.1109/ICC51166.2024.10622602