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  • Adaptive Spatial Modulation MIMO Based on Machine Learning

    • Authors: Yang, P.;  Advisor: -;  Participants: Xiao, Y.; Xiao, M.; Guan, Y.L.; Li, S.; Xiang, W. (2019)

  • In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multipleinput multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the K-nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS and PA parameter evaluation. ...
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  • Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective

    • Authors: Zhang, S.;  Advisor: -;  Participants: Bamakan, S. M. H.; Qu, Q.; Li, S. (2019)

  • With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical role in the field of personalized medicine. Personalized medicine (i.e., providing tailored medical treatment to individual patients through the identification of common features, including their genetics, inheritance, and lifestyle) has attracted the attention of many researchers in recent years. This paper provides an overview of the research progress in the application of learning methods, with a focus on deep learning in personalized medicine. In particular, three domains of applications are reviewed: drug development, disease characteristic identification, and therapeutic effect prediction. The main objective of this review is to cons...
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  • Signal Demodulation With Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset, and Algorithms

    • Authors: Ma, S.;  Advisor: -;  Participants: Dai, J.; Lu, S.; Li, H.; Zhang, H.; Du, C.; Li, S. (2019)

  • In this paper, we investigate the design and implementation of machine learning (ML)-based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a exible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Speci cally, the CNN-based demodulator converts the modulated signals to images and recognizes the signals by the image classi cation. The proposed DBN-based demodulator contains three restricted Boltzmann machines to extract the modulat...
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