MADS: 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/JETCAS.2019.2952145

MADS: 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

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

2 Citations (Scopus)

20 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
Pages (from-to)	712-722
Number of pages	11
Journal	IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Volume	9
Issue number	4
DOIs	https://doi.org/10.1109/JETCAS.2019.2952145
Publication status	Published - 1 Dec 2019
Publication type	A1 Journal article-refereed

Funding

Manuscript received August 6, 2019; revised September 28, 2019 and November 2, 2019; accepted November 3, 2019. Date of publication November 7, 2019; date of current version December 12, 2019. This work was supported in part by the U.S. National Science Foundation. This article was recommended by Guest Editor G. G. G. Lee. (Corresponding author: Lin Li.) L. Li, A. Sapio, and S. S. Bhattacharyya are with the ECE Department, University of Maryland, College Park, MD 20742 USA (e-mail: [email protected]; [email protected]; [email protected]@umd.edu).

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 2

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/JETCAS.2019.2952145

MADS Framework for Design 2019
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Accepted author manuscript, 3.24 MB

http://urn.fi/URN:NBN:fi:tuni-202011137948

Cite this

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title = "MADS: 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",

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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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MADS: A Framework for Design and Implementation of Adaptive Digital Predistortion Systems

Abstract

Funding

UN SDGs

Keywords

Publication forum classification

ASJC Scopus subject areas

Access to Document

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Cite this