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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| Vydané v: | ICC 2024 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS s. 545 - 550 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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IEEE
09.06.2024
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| Edícia: | IEEE International Conference on Communications |
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| ISBN: | 9781728190556, 172819055X, 9781728190549, 1728190541 |
| ISSN: | 1938-1883 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Prasad, Ganesh Johansson, Hakan Laskar, Rabul Hussain |
| Author_xml | – sequence: 1 givenname: Ganesh surname: Prasad fullname: Prasad, Ganesh email: ganesh.prasad@liu.se organization: Linköping University,Division of Communication Systems,Department of Electrical Engineering,Linköping,Sweden – sequence: 2 givenname: Hakan surname: Johansson fullname: Johansson, Hakan email: hakan.johansson@liu.se organization: Linköping University,Division of Communication Systems,Department of Electrical Engineering,Linköping,Sweden – sequence: 3 givenname: Rabul Hussain surname: Laskar fullname: Laskar, Rabul Hussain email: rhlaskar@ece.nits.ac.in organization: National Institute of Technology Silchar,Department of Electronics and Communication Engineering,Silchar,India |
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| Keywords | indirect learning architecture recursive prediction error method Digital predistortion signal processing |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | A Low-Complexity DPD to Fully Linearize the Power Amplifiers in a mMIMO Transmitter |
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