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.
Multidimensional synchronous dataflow (MDSDF) provides an effective model of computation for a variety of multidimensional DSP systems that have static dataflow structures. In this paper, we develop new methods for optimized implementation of MDSDF graphs on embedded platforms that employ multiple levels of parallelism to enhance performance at different levels of granularity. Our approach allows designers to systematically represent and transform multi-level parallelism specifications from a common, MDSDF-based application level model. We demonstrate our methods with a case study of image histogram implementation on a graphics processing unit (GPU). Experimental results from this study show that our approach can be used to derive fast GPU implementations, and enhance trade-off analysis during design space exploration.
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.
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