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Spatial Modulation for Beyond 5G Communications: Capacity Calculation and Link Adaptation

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Bibliographic Details
Title:	Spatial Modulation for Beyond 5G Communications: Capacity Calculation and Link Adaptation
Authors:	Anxo Tato, Carlos Mosquera
Source:	Proceedings, Vol 21, Iss 1, p 26 (2019)
Publisher Information:	MDPI AG
Publication Year:	2019
Collection:	Directory of Open Access Journals: DOAJ Articles
Subject Terms:	link adaptation, adaptive coding and modulation, spatial modulation, 5G, neural networks, machine learning, deep learning, General Works
Description:	Spatial Modulation (SM) is a candidate modulation scheme for beyond 5G communications systems due to its reduced hardware complexity and good trade-off between energy and spectral efficiency. This paper proposes two Machine Learning based solutions for easing the implementation of adaptive SM systems. On the one hand, a shallow neural network is shown to be an accurate and simple method for obtaining the capacity of SM. On the other hand, a deep neural network is proposed to select the coding rate in practical adaptive SM systems.
Document Type:	article in journal/newspaper
Language:	English
Relation:	https://www.mdpi.com/2504-3900/21/1/26; https://doaj.org/toc/2504-3900; https://doaj.org/article/74d98cbdc46e47908846549dc9a2b0cf
DOI:	10.3390/proceedings2019021026
Availability:	https://doi.org/10.3390/proceedings2019021026 https://doaj.org/article/74d98cbdc46e47908846549dc9a2b0cf
Accession Number:	edsbas.12AE5292
Database:	BASE

Description
Abstract:	Spatial Modulation (SM) is a candidate modulation scheme for beyond 5G communications systems due to its reduced hardware complexity and good trade-off between energy and spectral efficiency. This paper proposes two Machine Learning based solutions for easing the implementation of adaptive SM systems. On the one hand, a shallow neural network is shown to be an accurate and simple method for obtaining the capacity of SM. On the other hand, a deep neural network is proposed to select the coding rate in practical adaptive SM systems.
DOI:	10.3390/proceedings2019021026