This paper proposes a new blind channel identification and equalization method that exploits the cyclostationarity of oversampled communication signals to identify and equalize possibly nonminimum-phase channels without using training signals. Unlike most adaptive blind equalization methods, which often have convergence issues, the proposed algorithm is asymptotically exact. It uses second-order statistics, which may allow equalization with fewer symbols than methods relying on higher-order statistics. Simulations show promising performance for blind equalization of a three-ray multipath channel. The method uses the cyclostationarity of the received signal to identify the channel. By oversampling the signal, the algorithm can exploit the second-order statistics of the received signal to identify the channel. The algorithm is insensitive to timing recovery uncertainties and can be used to initialize various adaptive schemes. It can also be used to implement maximum likelihood sequence estimation to further reduce intersymbol interference. The algorithm relies only on second-order statistics of the received signal, making it more efficient than methods using higher-order statistics. It can be used to estimate channels with relatively rapid channel variation. The algorithm is also insensitive to the probability distribution of the source symbols, allowing for real or complex, continuous or discrete, or even Gaussian sources. The paper presents a new method for blind channel identification and equalization that uses the cyclostationarity of the received signal. The method is based on a vector representation of the signal and uses the second-order statistics of the received signal to identify the channel. The algorithm is asymptotically exact and can be used to initialize various adaptive schemes. It can also be used to implement maximum likelihood sequence estimation to further reduce intersymbol interference. The algorithm is tested in simulations, showing promising performance for blind equalization of a three-ray multipath channel. The results show that the algorithm can accurately estimate the channel even with a small number of symbols. The paper concludes that the proposed method is a significant advancement in blind equalization, offering a more efficient and accurate approach to channel identification and equalization.This paper proposes a new blind channel identification and equalization method that exploits the cyclostationarity of oversampled communication signals to identify and equalize possibly nonminimum-phase channels without using training signals. Unlike most adaptive blind equalization methods, which often have convergence issues, the proposed algorithm is asymptotically exact. It uses second-order statistics, which may allow equalization with fewer symbols than methods relying on higher-order statistics. Simulations show promising performance for blind equalization of a three-ray multipath channel. The method uses the cyclostationarity of the received signal to identify the channel. By oversampling the signal, the algorithm can exploit the second-order statistics of the received signal to identify the channel. The algorithm is insensitive to timing recovery uncertainties and can be used to initialize various adaptive schemes. It can also be used to implement maximum likelihood sequence estimation to further reduce intersymbol interference. The algorithm relies only on second-order statistics of the received signal, making it more efficient than methods using higher-order statistics. It can be used to estimate channels with relatively rapid channel variation. The algorithm is also insensitive to the probability distribution of the source symbols, allowing for real or complex, continuous or discrete, or even Gaussian sources. The paper presents a new method for blind channel identification and equalization that uses the cyclostationarity of the received signal. The method is based on a vector representation of the signal and uses the second-order statistics of the received signal to identify the channel. The algorithm is asymptotically exact and can be used to initialize various adaptive schemes. It can also be used to implement maximum likelihood sequence estimation to further reduce intersymbol interference. The algorithm is tested in simulations, showing promising performance for blind equalization of a three-ray multipath channel. The results show that the algorithm can accurately estimate the channel even with a small number of symbols. The paper concludes that the proposed method is a significant advancement in blind equalization, offering a more efficient and accurate approach to channel identification and equalization.