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

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
1 Downloads (Pure)


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 languageEnglish
Pages (from-to)712-722
Number of pages11
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Issue number4
Publication statusPublished - 1 Dec 2019
Publication typeA1 Journal article-refereed


  • dataflow modeling
  • digital predistortion
  • Markov decision processes
  • Smart systems

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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