Browsing by Author "Fayez, Almohanad Samir"
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- Design Space Decomposition for Cognitive and Software Defined RadiosFayez, Almohanad Samir (Virginia Tech, 2013-06-07)Software Defined Radios (SDRs) lend themselves to flexibility and extensibility because they
depend on software to implement radio functionality. Cognitive Engines (CEs) introduce
intelligence to radio by monitoring radio performance through a set of meters and configuring
the underlying radio design by modifying its knobs. In Cognitive Radio (CR) applications,
CEs intelligently monitor radio performance and reconfigure them to meet it application
and RF channel needs. While the issue of introducing computational knobs and meters
is mentioned in literature, there has been little work on the practical issues involved in
introducing such computational radio controls.
This dissertation decomposes the radio definition to reactive models for the CE domain
and real-time, or dataflow models, for the SDR domain. By allowing such design space
decomposition, CEs are able to define implementation independent radio graphs and rely on
a model transformation layer to transform reactive radio models to real-time radio models
for implementation. The definition of knobs and meters in the CE domain is based on
properties of the dataflow models used in implementing SDRs. A framework for developing
this work is presented, and proof of concept radio applications are discussed to demonstrate
how CEs can gain insight into computational aspects of their radio implementation during
their reconfiguration decision process. - Designing a Software Defined Radio to Run on a Heterogeneous ProcessorFayez, Almohanad Samir (Virginia Tech, 2011-04-25)Software Defined Radios (SDRs) are radio implementations in software versus the classic method of using discrete electronics. Considering the various classes of radio applications ranging from mobile-handsets to cellular base-stations, SDRs cover a wide range of power and computational needs. As a result, computing heterogeneity, in terms of Field-Programmable Gate Arrays (FPGAs), Digital Signal Processors (DSPs), and General Purpose Processors (GPPs), is needed to balance the computing and power needs of such radios. Whereas SDR represents radio implementation, Cognitive Radio (CR) represents a layer of intelligence and reasoning that derives reconfiguration of an SDR to suit an application's need. Realizing CR requires a new dimension for radios, dynamically creating new radio implementations during runtime so they can respond to changing channel and/or application needs. This thesis explores the use of integrated GPP and DSP based processors for realizing SDR and CR applications. With such processors a GPP realizes the mechanism driving radio reconfiguration, and a DSP is used to implement the SDR by performing the signal processing necessary. This thesis discusses issues related to implementing radios in this computing environment and presents a sample solution for integrating both processors to create SDR-based applications. The thesis presents a sample application running on a Texas Instrument (TI) OMAP3530 processor, utilizing its GPP and DSP cores, on a platform called the Beagleboard. For the application, the Center for Wireless Telecommunications' (CWT) Public Safety Cognitive Radio (PSCR) is ported, and an Android based touch screen interface is used for user interaction. In porting the PSCR to the Beagleboard USB bandwidth and memory access latency issues were the main system bottlenecks. Latency measurements of these interfaces are presented in the thesis to highlight those bottlenecks and can be used to drive GPP/DSP based system design using the Beagleboard.