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A Framework for Design and Implementation of Adaptive Digital Predistortion Systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
Number of pages5
ISBN (Electronic)9781538678848
Publication statusPublished - 1 Mar 2019
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Artificial Intelligence Circuits and Systems - Hsinchu, Taiwan, Province of China
Duration: 18 Mar 201920 Mar 2019


ConferenceIEEE International Conference on Artificial Intelligence Circuits and Systems
CountryTaiwan, Province of China


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.


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

Publication forum classification

Field of science, Statistics Finland