Research Article
Dataflow Modeling and Design for Cognitive Radio Networks
@INPROCEEDINGS{10.4108/icst.crowncom.2013.252125, author={Lai-Huei Wang and Shuvra Bhattacharyya and Aida Vosoughi and Joseph Cavallaro and Markku Juntti and Jani Boutellier and Olli Silven and Mikko Valkama}, title={Dataflow Modeling and Design for Cognitive Radio Networks}, proceedings={8th International Conference on Cognitive Radio Oriented Wireless Networks}, publisher={ICST}, proceedings_a={CROWNCOM}, year={2013}, month={11}, keywords={data flow modeling cognitive radio digital pre-distortion}, doi={10.4108/icst.crowncom.2013.252125} }- Lai-Huei Wang
Shuvra Bhattacharyya
Aida Vosoughi
Joseph Cavallaro
Markku Juntti
Jani Boutellier
Olli Silven
Mikko Valkama
Year: 2013
Dataflow Modeling and Design for Cognitive Radio Networks
CROWNCOM
IEEE
DOI: 10.4108/icst.crowncom.2013.252125
Abstract
Cognitive radio networks present challenges at many levels of design including configuration, control, and crosslayer optimization. In this paper, we focus primarily on dataflow representations to enable flexibility and reconfigurability in many of the baseband algorithms. Dataflow modeling will be important to provide a layer of abstraction and will be applied to generate flexible baseband representations for cognitive radio testbeds, including the Rice WARP platform. As RF frequency agility and reconfiguration for carrier aggregation are important goals for 4G LTE Advanced systems, we also focus on dataflow analysis for digital pre-distortion algorithms. A new design method called parameterized multidimensional design hierarchy mapping (PMDHM) is presented, along with initial speedup results from applying PMDHM in the mapping of channel estimation onto a GPU architecture.