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Adaptive Spatial Modulation MIMO Based on Machine Learning
Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China..
Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China..
KTH, Skolan för elektroteknik och datavetenskap (EECS), Teknisk informationsvetenskap.ORCID-id: 0000-0002-5407-0835
Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore..
Vise andre og tillknytning
2019 (engelsk)Inngår i: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 37, nr 9, s. 2117-2131Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 37, nr 9, s. 2117-2131
Emneord [en]
Index modulation, SM-MIMO, machine learning, neural network, link adaptation
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Identifikatorer
URN: urn:nbn:se:kth:diva-257802DOI: 10.1109/JSAC.2019.2929404ISI: 000481983100013Scopus ID: 2-s2.0-85071016115OAI: oai:DiVA.org:kth-257802DiVA, id: diva2:1350900
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QC 20190912

Tilgjengelig fra: 2019-09-12 Laget: 2019-09-12 Sist oppdatert: 2019-09-12bibliografisk kontrollert

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