Matching Items (2)
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- All Subjects: Networks
- All Subjects: MIMO
- Genre: Masters Thesis
- Member of: Theses and Dissertations
- Resource Type: Text
- Status: Published
Description
As the number of devices with wireless capabilities and the proximity of these devices to each other increases, better ways to handle the interference they cause need to be explored. Also important is for these devices to keep up with the demand for data rates while not compromising on industry established expectations of power consumption and mobility. Current methods of distributing the spectrum among all participants are expected to not cope with the demand in a very near future. In this thesis, the effect of employing sophisticated multiple-input, multiple-output (MIMO) systems in this regard is explored. The efficacy of systems which can make intelligent decisions on the transmission mode usage and power allocation to these modes becomes relevant in the current scenario, where the need for performance far exceeds the cost expendable on hardware. The effect of adding multiple antennas at either ends will be examined, the capacity of such systems and of networks comprised of many such participants will be evaluated. Methods of simulating said networks, and ways to achieve better performance by making intelligent transmission decisions will be proposed. Finally, a way of access control closer to the physical layer (a 'statistical MAC') and a possible metric to be used for such a MAC is suggested.
ContributorsThontadarya, Niranjan (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2014
Description
The human transcriptional regulatory machine utilizes hundreds of transcription factors which bind to specific genic sites resulting in either activation or repression of targeted genes. Networks comprised of nodes and edges can be constructed to model the relationships of regulators and their targets. Within these biological networks small enriched structural patterns containing at least three nodes can be identified as potential building blocks from which a network is organized. A first iteration computational pipeline was designed to generate a disease specific gene regulatory network for motif detection using established computational tools. The first goal was to identify motifs that can express themselves in a state that results in differential patient survival in one of the 32 different cancer types studied. This study identified issues for detecting strongly correlated motifs that also effect patient survival, yielding preliminary results for possible driving cancer etiology. Second, a comparison was performed for the topology of network motifs across multiple different data types to identify possible divergence from a conserved enrichment pattern in network perturbing diseases. The topology of enriched motifs across all the datasets converged upon a single conserved pattern reported in a previous study which did not appear to diverge dependent upon the type of disease. This report highlights possible methods to improve detection of disease driving motifs that can aid in identifying possible treatment targets in cancer. Finally, networks where only minimally perturbed, suggesting that regulatory programs were run from evolved circuits into a cancer context.
ContributorsStriker, Shawn Scott (Author) / Plaisier, Christopher (Thesis advisor) / Brafman, David (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2020