Dataflow modeling and design for cognitive radio networks

Cognitive radio networks present challenges at many levels of design including configuration, control, and cross-layer 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 predistortion 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.