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/symbol estimation-the proposed approach accomplishes direct symbol estimation without determining the channel characteristics. Based on a deterministic model, the new method can provide a closed-form solution to the symbol estimation using a small set of data samples, Which makes it particularly suitable for wireless applications with fast changing environments, Moreover, if the symbols belong to a finite alphabet, e.g., BPSK or QPSK, our approach can be extended to handle the symbol estimation for multiple sources, Computer simulations and field RF experiments were conducted to demonstrate the performance of the proposed method, The results are compared to the Cramer-Rao lower bound of the symbol estimates derived in this paper.