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Modulation type is one of the most important characteristics used in signal waveform identification for wireless communications. In this paper, a cepstral algorithm for Automatic Digital Modulation Recognition (ADMR) is proposed with adaptive modulation in Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed algorithm is verified using classifiers for the modulated signals. This algorithm uses Mel Frequency Cepstral Coefficients (MFCCs) to extract the features of the modulated signal and a multi-layer feed-forward neural network to classify the modulation type and its order. The proposed classifier is capable of recognizing the modulation scheme with high accuracy over a wide Signal-to-Noise Ratio (SNR) range in the presence of Additive White Gaussian Noise (AWGN). As the demand of high-quality service in next-generation wireless communication systems increases, a high performance of data transmission requires an increase in spectral efficiency and an improvement in error performance of wireless communication systems. One of the promising approaches to 4G is Adaptive OFDM (AOFDM). In AOFDM, an adaptive transmission scheme is employed according to the channel fading conditions to improve the performance. The performance of adaptive modulation systems depends on the decision-making logic. Adaptive modulation systems using hardware decision-making circuits are inefficient to decide or change the modulation scheme according to the given conditions. Using a fuzzy logic in the decision-making interface makes the system more efficient.