Subspace-Based Blind Channel Estimation by Separating Real and Imaginary Symbols for Cyclic-Prefixed Single-Carrier Systems

Abstract

Blind channel estimation based on a subspace-based algorithm for single-input single-output cyclic-prefixed single-carrier systems is proposed in this brief. The proposed method is different from conventional subspace-based approaches, which exploit the original complex structure of the received data symbols. In contrast, we separate the real part and the imaginary part of the received complex symbols and then exploit these two kinds of symbols to construct the signal model. The noise subspace can then be established to estimate the channel impulse response (CIR) using a subspace algorithm when real symbols, such as binary phase shift keying or pulse amplitude modulation symbols, are applied. The real part and the imaginary part of the CIR can be estimated individually and simultaneously with only a sign ambiguity. With the aid of repetition index, the proposed method is workable even if few data blocks are available. Simulation results demonstrate that the proposed approach outperforms conventional methods in normalized mean-squared error under static channel environments.