Adaptive spatial modulation MIMO based on machine learning

In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multiple-input 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. Furthermore, we investigate the design of feature vectors for the SLC and DNN approaches and propose a novel feature vector generator to match the specific transmission mode of SM. As a further advance, our proposed approaches are extended to other adaptive index modulation (IM) schemes, e.g., adaptive modulation (AM) aided orthogonal frequency division multiplexing with IM (OFDM-IM). Our simulation results show that the SLC and DNN-based adaptive SM-MIMO systems outperform many conventional optimization-driven designs and are capable of achieving a near-optimal performance with a significantly lower complexity.This work was supported in part by the National Science Foundation of China under Grant 61876033 and Grant 61671131, in part by the Foundation Project of National Key Laboratory of Science and Technology on Communications under Grant 9140C020108140C02005, and in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2015KYQD003.The work of M. Xiao was supported in part by the SSF project “High-Reliable Low-Latency Industrial Wireless Communications” and in part by EU Marie Sklodowska-Curie Actions Project “High-Reliability Low-Latency Communications With Network Coding,” and ERA-NET, “SMART-MLA.