BBAuthors: 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... 
