Adapting Cellular Networks to Whitespaces Spectrum PROJECT TITLE :Adapting Cellular Networks to Whitespaces SpectrumABSTRACT:TV Whitespaces, recently opened by the Federal Communications Commission (FCC) for unlicensed use, are seen as a potential cellular offload and/or standalone mechanism, particularly in dense metros where the demand for throughput is high. During this paper, we use real data collected from whitespaces databases to empirically demonstrate features distinctive to whitespaces-power-spectrum tradeoff and spatial variation in spectrum availability. From this study, we have a tendency to conclude the need for whitespaces-specific diversifications to cellular networks thus on be ready to extract maximum throughput and guarantee reliability. To tackle the consequences of the ability-spectrum tradeoff, we tend to propose a novel base-station design that specifically uses low-power transmitters as a means that to maximise throughput. This design co-locates and networks together several low-powered mode-I devices to act as a multiple-antenna array. We have a tendency to estimate the scale of the array required to satisfy typical rate targets, and show that the array style significantly outperforms traditional designs in terms of throughput for a given price. We have a tendency to then turn our attention to spatial variability and study its impact on the problem of locating base stations in a whitespaces network. Here, we propose spectrum-aware placement algorithms for whitespaces, which account for this spatial variability together with key parameters like user density. We show that such algorithms clearly outperform ancient placement algorithms and improve network coverage in this band. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Removal of residual cavitation nuclei to enhance histotripsy erosion of model urinary stones Computational Feasibility Study of Contrast-Enhanced Thermoacoustic Imaging for Breast Cancer Detection Using Realistic Numerical Breast Phantoms