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Abstract: Channel Estimation is an essential component in applications such as radarand data communication. In multi path time varying environments, it isnecessary to estimate time-shifts, scale-shifts the wideband equivalent ofDoppler-shifts, and the gains-phases of each of the multiple paths. Withrecent advances in sparse estimation or -compressive sensing-, new estimationtechniques have emerged which yield more accurate estimates of these channelparameters than traditional strategies. These estimation strategies, however,restrict potential estimates of time-shifts and scale-shifts to a finite set ofvalues separated by a choice of grid spacing. A small grid spacing increasesthe number of potential estimates, thus lowering the quantization error, butalso increases complexity and estimation time. Conversely, a large grid spacinglowers the number of potential estimates, thus lowering the complexity andestimation time, but increases the quantization error. In this thesis, wederive an expression which relates the choice of grid spacing to themean-squared quantization error. Furthermore, we consider the case whenscale-shifts are approximated by Doppler-shifts, and derive a similarexpression relating the choice of the grid spacing and the quantization error.Using insights gained from these expressions, we further explore the effects ofthe choice and grid spacing, and examine when a wideband model can be wellapproximated by a narrowband model.

Author: Brian Carroll

Source: https://arxiv.org/


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