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Model-based design and implementation of an adaptive digital predistortion filter

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


Original languageEnglish
Title of host publicationIEEE Workshop on Signal Processing Systems (SiPS)
PublisherInstitute of Electrical and Electronics Engineers, IEEE
Number of pages6
ISBN (Print)978-1-4673-9604-2
Publication statusPublished - Oct 2015
Publication typeA4 Article in a conference publication
EventIEEE Workshop on Signal Processing Systems -
Duration: 1 Jan 1900 → …


ConferenceIEEE Workshop on Signal Processing Systems
Period1/01/00 → …


Dataflow models of computation are widely used for modeling signal processing systems. These models have inherent concurrency and the task (actor) execution depends only on the availability of the input data (tokens). This property of dataflow models can be exploited for dynamic power management by automatically switching off the actors with no available input tokens. This idea is applied in this paper for efficient modeling and implementation of an adaptive Digital Predistortion (DPD) filter. The DPD filter is required to operate with different profiles under varying operation scenarios, hence requiring a methodology to manage power dynamically. The paper presents a dataflow model for Adaptive Digital Predistortion based on the Core Functional Dataflow (CFDF) model of computation using the Light Weight Dataflow (LWDF) programming methodology. The paper also provides a methodology for dynamic power management under the dataflow paradigm. To the authors' best knowledge, this work is the first to integrate dataflow-based power management systematically in the context of adaptive DPD implementation.