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This article describes recent advances in cognitive communications. We combine the concepts of signal processing, communications, pattern classification, and machine learning to make dynamic use of the spectrum, such that the emanated signals do not interfere with the existing ones. Unlike other programs such as neXt Generation communications of the Defense Advanced Research Projects Agency, where radio scene analysis is performed to find the spectrum holes or the white space, we make use of the white, as well as the gray space for non- interfering signal transmission. We examine the possibility of employing machine perception and autonomous machine learning technologies to the autonomous design and analysis of air interfaces. The underlying premise is that a learning module will facilitate adaptation in the standard classification process so that the presence of new types of waveforms can be detected, features that best facilitate classification of the previously and newly identified signals can be determined, and waveforms can be generated by using the basis-set orthogonal to the ones present in the environment. Incremental learning and prediction allows knowledge enhancement as more snapshots of data are processed, resulting in improved decisions. Some of the contributions of this project include technological advances in signal detection, feature identification, signal classification, sub-space tracking, adaptive waveform design, machine learning, and prediction.