Parameterized Sets of Dataflow Modes And Their Application to Implementation of Cognitive Radio Systems

Shuoxin Lin, Lai-Huei Wang, Aida Vosoughi, Joseph R. Cavallaro, Markku Juntti, Jani Boutellier, Olli Silven, Mikko Valkama, Shuvra S. Bhattacharyya

    Research output: Contribution to journalArticleScientificpeer-review

    10 Citations (Scopus)

    Abstract

    Cognitive radio networks present challenges at many levels of design, including configuration, control, and cross-layer optimization. To meet requirements of bandwidth, flexibility and reconfigurability, systematic methods to model and analyze cognitive radio designs on signal processing platforms are desired. To help address these challenges, we present in this paper a novel dataflow modeling technique, called parameterized set of modes (PSM). PSMs allow efficient representation, manipulation and application of related groups of processing configurations for functional design components in signal processing systems. PSMs lead to more concise formulations of actor behavior, and a unified modeling methodology for applying a variety of techniques for efficient implementation. We develop the formal foundations of PSM-based modeling, and demonstrate its utility through two case studies involving the mapping of reconfigurable wireless communication functionality into efficient implementations.
    Translated title of the contributionParameterized Sets of Dataflow Modes And Their Application to Implementation of Cognitive Radio Systems
    Original languageEnglish
    Pages (from-to)3-18
    Number of pages16
    JournalJournal of Signal Processing Systems
    Volume80
    Issue number1
    DOIs
    Publication statusPublished - 2015
    Publication typeA1 Journal article-refereed

    Publication forum classification

    • Publication forum level 1

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