CLOSED-FORM BLIND SYMBOL ESTIMATION IN DIGITAL-COMMUNICATIONS

We study the blind symbol estimation problem in digital communications and propose a novel algorithm by exploiting a special data structure of an oversampled system output, Unlike most equalization schemes that involve two stages-channel identification and channel equalization/sy mbol estimation-the proposed approach accomplishes direct symbol estim ation without determining the channel characteristics. Based on a dete rministic model, the new method can provide a closed-form solution to the symbol estimation using a small set of data samples, Which makes i t particularly suitable for wireless applications with fast changing e nvironments, Moreover, if the symbols belong to a finite alphabet, e.g ., BPSK or QPSK, our approach can be extended to handle the symbol est imation for multiple sources, Computer simulations and field RF experi ments were conducted to demonstrate the performance of the proposed me thod, The results are compared to the Cramer-Rao lower bound of the sy mbol estimates derived in this paper.