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Constant False Alarm Rate (CFAR) detection based estimators with applications to sparse wireless channels
We provide Constant False Alarm Rate (CFAR) based thresholding methods for training based channel impulse response (CIR) estimation algorithms for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols. After obtaining the CIR estimation by using known methods in the literature, there are estimation errors which causes performance loss at equalizers. The channel estimation error can be seen as .noise. on CIR estimations and CFAR based thresholding methods, which are used in radar systems to decide the presence of a target, can effectively overcome this problem. CFAR based methods are based on determining threshold values which are computed by distribution of channel noise. We provide exact and approximate distribution of channel noise appear at CIR estimate schemes. We applied Cell Averaging-CFAR (CA-CFAR) and Order Statistic-CFAR (OSCFAR) methods on the CIR estimations. The performance of the CFAR estimators are then compared by their Least Square error in the channel estimates. The Signal to Interference plus Noise Ratio (SINR) performance of the decision feedback equalizers (DFE), of which the tap values are calculated based on the CFAR estimators, are also provided.