Abstract
In this paper, deep learning (DL)-aided data detection of spatial multiplexing (SMX) multiple-input multiple-output (MIMO) transmission with index modulation (IM) (Deep-SMX-IM) has been proposed. Deep-SMX-IM has been constructed by combining a zero-forcing (ZF) detector and DL technique. The proposed method uses the significant advantages of DL techniques to learn transmission characteristics of the frequency and spatial domains. Furthermore, thanks to using subblock-based detection provided by IM, Deep-SMX-IM is a straightforward method, which eventually reveals reduced complexity. It has been shown that Deep-SMX-IM has significant error performance gains compared to ZF detector without increasing computational complexity for different system configurations.
Original language | English |
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Title of host publication | Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers |
Editors | Éric Renault, Selma Boumerdassi, Paul Mühlethaler |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 226-236 |
Number of pages | 11 |
ISBN (Print) | 9783030708658 |
DOIs | |
Publication status | Published - 2021 |
Event | 3rd International Conference on Machine Learning for Networking, MLN 2020 - Paris, France Duration: 24 Nov 2020 → 26 Nov 2020 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12629 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 3rd International Conference on Machine Learning for Networking, MLN 2020 |
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Country/Territory | France |
City | Paris |
Period | 24/11/20 → 26/11/20 |
Bibliographical note
Publisher Copyright:© 2021, Springer Nature Switzerland AG.
Keywords
- Deep learning
- GFDM
- Index modulation
- OFDM
- Spatial multiplexing