Adaptive spatial modulation MIMO based on machine learning

Yang, Ping, Xiao, Yue, Xiao, Ming, Guan, Yong Liang, Li, Shaoqian, and Xiang, Wei (2019) Adaptive spatial modulation MIMO based on machine learning. IEEE Journal on Selected Areas in Communications, 37 (9). pp. 2117-2131.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: https://doi.org/10.1109/JSAC.2019.292940...
11


Abstract

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.

Item ID: 61737
Item Type: Article (Research - C1)
ISSN: 1558-0008
Keywords: index modulation, link adaptation, machine learning, neural network, SM-MIMO
Copyright Information: © 2019 IEEE.
Funders: National Science Foundation of China (NSFC), National Key Laboratory of Science and Technology on Communications (NKLSTC), Central Universities (CU)
Projects and Grants: NSFC Grant 61876033 and Grant 61671131, NKLSTC Foundation Project Grant 9140C020108140C02005, CU Fundamental Research Funds Grant ZYGX2015KYQD003
Date Deposited: 12 May 2020 19:50
FoR Codes: 10 TECHNOLOGY > 1005 Communications Technologies > 100510 Wireless Communications @ 100%
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page