Exact Sphere Constrained Maximum Likelihood Detection of Parameters for Cdma
نویسنده
چکیده
We describe a method for Maximum Likelihood (ML) parameter estimation corrupted by additive white Gaussian noise. The ML cost function is maximized over the constraint that the detected data vector lie on the sphere. The results are compared with MMSE and with [2]. Simulations results shows superior performance of our method comparing to both the methods.
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تاریخ انتشار 2003