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2019 | OriginalPaper | Chapter

Deep Learning-Based Space Shift Keying Systems

Authors : Yue Zhang, Xuesi Wang, Jintao Wang, Yonglin Xue, Jian Song

Published in: Artificial Intelligence for Communications and Networks

Publisher: Springer International Publishing

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Abstract

To handle the performance degradation of space shift keying (SSK) systems under practical non-Gaussian channels, we propose a deep neural network model in which an auto-encoder (AE) is developed to design proper constellations and corresponding demodulation. With full knowledge of channel statistics, the transmitter and receiver are jointly optimized in our scheme. By representing the SSK system as an AE, we consider the cross-entropy loss function for antenna index and formulate the overall pipeline using deep learning techniques. Moreover, our implementation can be adopted in several noise conditions successfully. Results confirm that our model outperforms the maximum likelihood (ML) detection scheme in terms of block error rates (BLER).

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previous chapter Millimeter Wave Massive MIMO Channel Estimation and Tracking
next chapter Non-orthogonal Multiple Access Enabled Power Allocation for Cooperative Jamming in Wireless Networks
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Metadata
Title
Deep Learning-Based Space Shift Keying Systems
Authors
Yue Zhang
Xuesi Wang
Jintao Wang
Yonglin Xue
Jian Song
Copyright Year
2019
Book
Artificial Intelligence for Communications and Networks

Print ISBN: 978-3-030-22967-2

Electronic ISBN: 978-3-030-22968-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-22968-9_7

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