On the other hand, recent breakthroughs in machine learning have motivated researchers to apply relevant techniques for bringing intelligence to communication systems and networks. Potentially, machine learning techniques can be exploited to either enhance the individual communications functional blocks (e.g., synchronization, channel estimation and equalization) or even replacing the traditional modularized architecture with an all-in-one integrated solution directly targeting the end-to-end performance. Our group contributes to this emerging area by developing intelligent communications system design via integrating cutting-edge learning techniques and wireless communications theory in the same vein. Our initial work on this area includes the application of statistical inference to tackle the primitive hardware and signal processing limitations of backscatter sensors [ELC5], and the application of machine learning on Grassmann manifold to facilitate automatic constellation recognization and non-coherent MIMO signal recovery [ELC6].

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Recently, our group also developed an interest in an emerging trend in machine learning society that advocates geometry-awareness embedding in the learning algorithm design. Our research in this field has led to a tutorial paper [ELC7] which introduces a particularly new area along with the trend, called Grassmannian learning. It is a new machine learning paradigm that involves subspace-structured features, Grassmannian metrics or Grassmannian-constrained objective functions. The new learning paradigm finds a wide range of signal-processing applications in the areas of computer vision, natural language processing, and wireless communications and thus has a broad impact.