Data-Parallel Implementation of Reconfigurable Digital Predistortion on a Mobile GPU

Amanullah Ghazi; Jani Boutellier; Lauri Anttila; Markku Juntti; Mikko Valkama

doi:10.1109/ACSSC.2015.7421110

Data-Parallel Implementation of Reconfigurable Digital Predistortion on a Mobile GPU

Amanullah Ghazi, Jani Boutellier, Lauri Anttila, Markku Juntti, Mikko Valkama

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

Abstract

3GPP LTE-A offers new technologies such as non-contiguous carrier allocation for improving radio spectrum utilization. However, implementation of these technologies is challenging because of intermodulation distortion caused by non- linearity of components. Digital Predistortion (DPD) offers a way for compensating for these nonlinearities by modifying the digital baseband signal. As most consumer-oriented mobile devices are equipped with powerful Graphics Processing Units (GPUs), it has become possible to implement DPD functionality to such devices with no additional hardware cost. In this paper, we propose data- parallel, reconfigurable predistortion and measure its performance on mobile GPUs: Qualcomm Adreno 330 and ARM Mali T628.

Original language	English
Title of host publication	2015 49th Asilomar Conference on Signals, Systems and Computers
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-4673-8576-3
DOIs	https://doi.org/10.1109/ACSSC.2015.7421110
Publication status	Published - Feb 2016
Publication type	A4 Article in conference proceedings
Event	Asilomar Conference on Signals, Systems and Computers - Duration: 1 Jan 1900 → …

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Conference	Asilomar Conference on Signals, Systems and Computers
Period	1/01/00 → …

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10.1109/ACSSC.2015.7421110

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title = "Data-Parallel Implementation of Reconfigurable Digital Predistortion on a Mobile GPU",

abstract = "3GPP LTE-A offers new technologies such as non-contiguous carrier allocation for improving radio spectrum utilization. However, implementation of these technologies is challenging because of intermodulation distortion caused by non- linearity of components. Digital Predistortion (DPD) offers a way for compensating for these nonlinearities by modifying the digital baseband signal. As most consumer-oriented mobile devices are equipped with powerful Graphics Processing Units (GPUs), it has become possible to implement DPD functionality to such devices with no additional hardware cost. In this paper, we propose data- parallel, reconfigurable predistortion and measure its performance on mobile GPUs: Qualcomm Adreno 330 and ARM Mali T628.",

author = "Amanullah Ghazi and Jani Boutellier and Lauri Anttila and Markku Juntti and Mikko Valkama",

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doi = "10.1109/ACSSC.2015.7421110",

language = "English",

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AU - Ghazi, Amanullah

AU - Boutellier, Jani

AU - Anttila, Lauri

AU - Juntti, Markku

AU - Valkama, Mikko

PY - 2016/2

Y1 - 2016/2

N2 - 3GPP LTE-A offers new technologies such as non-contiguous carrier allocation for improving radio spectrum utilization. However, implementation of these technologies is challenging because of intermodulation distortion caused by non- linearity of components. Digital Predistortion (DPD) offers a way for compensating for these nonlinearities by modifying the digital baseband signal. As most consumer-oriented mobile devices are equipped with powerful Graphics Processing Units (GPUs), it has become possible to implement DPD functionality to such devices with no additional hardware cost. In this paper, we propose data- parallel, reconfigurable predistortion and measure its performance on mobile GPUs: Qualcomm Adreno 330 and ARM Mali T628.

AB - 3GPP LTE-A offers new technologies such as non-contiguous carrier allocation for improving radio spectrum utilization. However, implementation of these technologies is challenging because of intermodulation distortion caused by non- linearity of components. Digital Predistortion (DPD) offers a way for compensating for these nonlinearities by modifying the digital baseband signal. As most consumer-oriented mobile devices are equipped with powerful Graphics Processing Units (GPUs), it has become possible to implement DPD functionality to such devices with no additional hardware cost. In this paper, we propose data- parallel, reconfigurable predistortion and measure its performance on mobile GPUs: Qualcomm Adreno 330 and ARM Mali T628.

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DO - 10.1109/ACSSC.2015.7421110

M3 - Conference contribution

BT - 2015 49th Asilomar Conference on Signals, Systems and Computers

PB - IEEE

T2 - Asilomar Conference on Signals, Systems and Computers

Y2 - 1 January 1900

ER -

Data-Parallel Implementation of Reconfigurable Digital Predistortion on a Mobile GPU

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