Cyclostationarity-based blind classification of STBCs for cognitive radio systems

Signal classification is a major task of a cognitive radio. This paper proposes a novel cyclostationarity-based algorithm for the blind classification of space time block codes (STBCs) and derives analytical expressions for the second-order cyclic statistics used as the basis of the algorithm. Monte Carlo simulation results demonstrate a good classification performance with low sensitivity to phase noise and the channel Doppler shift. The proposed approach avoids the need for a priori knowledge of the channel coefficients, carrier phase, and timing offsets. Moreover, it does not need accurate information about the transmission data rate and carrier frequency offset.