Minimum - Entropy Blind Acquisition / Equalizationfor
نویسنده
چکیده
In this paper, we consider blind estimation of linear chip-spaced receivers for the demodulation of a particular short-code DS-CDMA mobile user under multipath propagation and in the absence of timing information. We propose a family of schemes for blind acquisition and equalization based on Donoho's Minimum Entropy principle and propose a speciic algorithm that uses the second-and fourth-order moments of a pre-whitened chip-rate received signal. The proposed algorithm can be considered a near-far resistant initialization procedure for, and application of, the Constant Modulus Algorithm (CMA) to DS-CDMA.
منابع مشابه
Minimum-entropy Blind Acquisition/equalization for Uplink Ds-cdma
In this paper, we consider blind estimation of linear chip-spaced receivers for the demodulation of a particular short-code DS-CDMA mobile user under multipath propagation and in the absence of timing information. We propose a family of schemes for blind acquisition and equalization based on Donoho's Minimum Entropy principle and propose a speciic algorithm that uses the second-and fourth-order...
متن کاملMinimum Entropy Algorithms for Source Separation
The minimum entropy or maximum likelihood estimation can be utilized in blind source separation problem. Based on the local generalized Gaussian probability density model, a set of general anti-Hebbian rule can be derived. This set of adaptation rules give promising results when we test the real recordings.
متن کاملAn On-line Adaptation Algorithm for Adaptive System Training with Minimum Error Entropy: Stochastic Information Gradient
We have recently reported on the use of minimum error entropy criterion as an alternative to minimum square error (MSE) in supervised adaptive system training. A nonparametric estimator for Renyi’s entropy was formulated by employing Parzen windowing. This formulation revealed interesting insights about the process of information theoretical learning, namely information potential and informatio...
متن کاملAdaptive On - Line Learning Algorithms for Blind Separation | Maximum Entropy and Minimum Mutual Information
There are two major approaches for blind separation: Maximum Entropy (ME) and Minimum Mutual Information (MMI). Both can be implemented by the stochastic gradient descent method for obtaining the de-mixing matrix. The MI is the contrast function for blind separation while the entropy is not. To justify the ME, the relation between ME and MMI is rstly elucidated by calculating the rst derivative...
متن کاملAdaptive On - Line Learning Algorithms for Blind Separation | Maximum Entropy and Minimum Mutual
There are two major approaches for blind separation: Maximum Entropy (ME) and Minimum Mutual Information (MMI). Both can be implemented by the stochastic gradient descent method for obtaining the de-mixing matrix. The MI is the contrast function for blind separation while the entropy is not. To justify the ME, the relation between ME and MMI is rstly elucidated by calculating the rst derivative...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998