A Framework for Design and Implementation of Adaptive Digital Predistortion Systems

Lin Li; Peter Deaville; Adrian Sapio; Lauri Anttila; Mikko Valkama; Marilyn Wolf; Shuvra S. Bhattacharyya

doi:10.1109/AICAS.2019.8771476

A Framework for Design and Implementation of Adaptive Digital Predistortion Systems

Lin Li
, Peter Deaville
, Adrian Sapio
, Lauri Anttila
, Mikko Valkama
, Marilyn Wolf
, Shuvra S. Bhattacharyya

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

2 Citations (Scopus)

52 Downloads (Pure)

Abstract

Digital predistortion (DPD) has important applications in wireless communication for smart systems, such as, for example, in Internet of Things (IoT) applications for smart cities. DPD is used in wireless communication transmitters to counteract distortions that arise from nonlinearities, such as those related to amplifier characteristics and local oscillator leakage. In this paper, we propose an algorithm-architecture-integrated framework for design and implementation of adaptive DPD systems. The proposed framework provides energy-efficient, real-time DPD performance, and enables efficient reconfiguration of DPD architectures so that communication can be dynamically optimized based on time-varying communication requirements. Our adaptive DPD design framework applies Markov Decision Processes (MDPs) in novel ways to generate optimized runtime control policies for DPD systems. We present a GPU-based adaptive DPD system that is derived using our design framework, and demonstrate its efficiency through extensive experiments.

Original language	English
Title of host publication	Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
Publisher	IEEE
Pages	112-116
Number of pages	5
ISBN (Electronic)	9781538678848
DOIs	https://doi.org/10.1109/AICAS.2019.8771476
Publication status	Published - 1 Mar 2019
Publication type	A4 Article in conference proceedings
Event	IEEE International Conference on Artificial Intelligence Circuits and Systems - Hsinchu, Taiwan, Province of China Duration: 18 Mar 2019 → 20 Mar 2019

Conference

Conference	IEEE International Conference on Artificial Intelligence Circuits and Systems
Country/Territory	Taiwan, Province of China
City	Hsinchu
Period	18/03/19 → 20/03/19

Funding

This research was sponsored in part by the US National Science Foundation.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

dataflow modeling
digital predistortion
Markov decision processes
Smart systems

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Artificial Intelligence
Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/AICAS.2019.8771476

DPD_framework_AICAS_2019Accepted author manuscript, 840 KB

http://urn.fi/URN:NBN:fi:tuni-202012229120

Cite this

@inproceedings{418fa5fd9e544f6b828ca53aa2b9b0d6,

title = "A Framework for Design and Implementation of Adaptive Digital Predistortion Systems",

abstract = "Digital predistortion (DPD) has important applications in wireless communication for smart systems, such as, for example, in Internet of Things (IoT) applications for smart cities. DPD is used in wireless communication transmitters to counteract distortions that arise from nonlinearities, such as those related to amplifier characteristics and local oscillator leakage. In this paper, we propose an algorithm-architecture-integrated framework for design and implementation of adaptive DPD systems. The proposed framework provides energy-efficient, real-time DPD performance, and enables efficient reconfiguration of DPD architectures so that communication can be dynamically optimized based on time-varying communication requirements. Our adaptive DPD design framework applies Markov Decision Processes (MDPs) in novel ways to generate optimized runtime control policies for DPD systems. We present a GPU-based adaptive DPD system that is derived using our design framework, and demonstrate its efficiency through extensive experiments.",

keywords = "dataflow modeling, digital predistortion, Markov decision processes, Smart systems",

author = "Lin Li and Peter Deaville and Adrian Sapio and Lauri Anttila and Mikko Valkama and Marilyn Wolf and Bhattacharyya, \{Shuvra S.\}",

year = "2019",

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doi = "10.1109/AICAS.2019.8771476",

language = "English",

pages = "112--116",

booktitle = "Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Artificial Intelligence Circuits and Systems ; Conference date: 18-03-2019 Through 20-03-2019",

}

Li, L, Deaville, P, Sapio, A, Anttila, L , Valkama, M, Wolf, M & Bhattacharyya, SS 2019, A Framework for Design and Implementation of Adaptive Digital Predistortion Systems. in Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. IEEE, pp. 112-116, IEEE International Conference on Artificial Intelligence Circuits and Systems, Hsinchu, Taiwan, Province of China, 18/03/19. https://doi.org/10.1109/AICAS.2019.8771476

A Framework for Design and Implementation of Adaptive Digital Predistortion Systems. / Li, Lin; Deaville, Peter; Sapio, Adrian et al.
Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. IEEE, 2019. p. 112-116.

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

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AU - Li, Lin

AU - Deaville, Peter

AU - Sapio, Adrian

AU - Anttila, Lauri

AU - Valkama, Mikko

AU - Wolf, Marilyn

AU - Bhattacharyya, Shuvra S.

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AB - Digital predistortion (DPD) has important applications in wireless communication for smart systems, such as, for example, in Internet of Things (IoT) applications for smart cities. DPD is used in wireless communication transmitters to counteract distortions that arise from nonlinearities, such as those related to amplifier characteristics and local oscillator leakage. In this paper, we propose an algorithm-architecture-integrated framework for design and implementation of adaptive DPD systems. The proposed framework provides energy-efficient, real-time DPD performance, and enables efficient reconfiguration of DPD architectures so that communication can be dynamically optimized based on time-varying communication requirements. Our adaptive DPD design framework applies Markov Decision Processes (MDPs) in novel ways to generate optimized runtime control policies for DPD systems. We present a GPU-based adaptive DPD system that is derived using our design framework, and demonstrate its efficiency through extensive experiments.

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KW - Markov decision processes

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M3 - Conference contribution

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BT - Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

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T2 - IEEE International Conference on Artificial Intelligence Circuits and Systems

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ER -

A Framework for Design and Implementation of Adaptive Digital Predistortion Systems

Abstract

Conference

Funding

UN SDGs

Keywords

Publication forum classification

ASJC Scopus subject areas

Access to Document

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Cite this