Deep Learning-Aided Spatial Multiplexing with Index Modulation

Merve Turhan; Ersin Öztürk; Hakan Ali Çırpan

doi:10.1007/978-3-030-70866-5_14

Deep Learning-Aided Spatial Multiplexing with Index Modulation

Merve Turhan^*
, Ersin Öztürk
, Hakan Ali Çırpan

^*Bu çalışma için yazışmadan sorumlu yazar

Elektronik ve Hab.Müh.Bölümü

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers
Editörler	Éric Renault, Selma Boumerdassi, Paul Mühlethaler
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	226-236
Sayfa sayısı	11
ISBN (Basılı)	9783030708658
DOI'lar	https://doi.org/10.1007/978-3-030-70866-5_14
Yayın durumu	Yayınlandı - 2021
Etkinlik	3rd International Conference on Machine Learning for Networking, MLN 2020 - Paris, France Süre: 24 Kas 2020 → 26 Kas 2020

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim	12629 LNCS
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	3rd International Conference on Machine Learning for Networking, MLN 2020
Ülke/Bölge	France
Şehir	Paris
Periyot	24/11/20 → 26/11/20

Bibliyografik not

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Belgeye Erişim

10.1007/978-3-030-70866-5_14

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Turhan, M., Öztürk, E., & Çırpan, H. A. (2021). Deep Learning-Aided Spatial Multiplexing with Index Modulation. In É. Renault, S. Boumerdassi, & P. Mühlethaler (Eds.), Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers (pp. 226-236). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 12629 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-70866-5_14

Turhan, Merve ; Öztürk, Ersin ; Çırpan, Hakan Ali. / Deep Learning-Aided Spatial Multiplexing with Index Modulation. Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers. editör / Éric Renault ; Selma Boumerdassi ; Paul Mühlethaler. Springer Science and Business Media Deutschland GmbH, 2021. pp. 226-236 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{24fdf4793cf34ffc9131f7de056e45ee,

title = "Deep Learning-Aided Spatial Multiplexing with Index Modulation",

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.",

keywords = "Deep learning, GFDM, Index modulation, OFDM, Spatial multiplexing",

author = "Merve Turhan and Ersin {\"O}zt{\"u}rk and {\c C}ırpan, \{Hakan Ali\}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 3rd International Conference on Machine Learning for Networking, MLN 2020 ; Conference date: 24-11-2020 Through 26-11-2020",

year = "2021",

doi = "10.1007/978-3-030-70866-5\_14",

language = "English",

isbn = "9783030708658",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "226--236",

editor = "{\'E}ric Renault and Selma Boumerdassi and Paul M{\"u}hlethaler",

booktitle = "Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers",

address = "Germany",

}

Turhan, M, Öztürk, E & Çırpan, HA 2021, Deep Learning-Aided Spatial Multiplexing with Index Modulation. in É Renault, S Boumerdassi & P Mühlethaler (eds), Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), hac. 12629 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 226-236, 3rd International Conference on Machine Learning for Networking, MLN 2020, Paris, France, 24/11/20. https://doi.org/10.1007/978-3-030-70866-5_14

Deep Learning-Aided Spatial Multiplexing with Index Modulation. / Turhan, Merve; Öztürk, Ersin; Çırpan, Hakan Ali.
Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers. ed. / Éric Renault; Selma Boumerdassi; Paul Mühlethaler. Springer Science and Business Media Deutschland GmbH, 2021. p. 226-236 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 12629 LNCS).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Deep Learning-Aided Spatial Multiplexing with Index Modulation

AU - Turhan, Merve

AU - Öztürk, Ersin

AU - Çırpan, Hakan Ali

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Deep learning

KW - GFDM

KW - Index modulation

KW - OFDM

KW - Spatial multiplexing

UR - https://www.scopus.com/pages/publications/85103547978

U2 - 10.1007/978-3-030-70866-5_14

DO - 10.1007/978-3-030-70866-5_14

M3 - Conference contribution

AN - SCOPUS:85103547978

SN - 9783030708658

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 226

EP - 236

BT - Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers

A2 - Renault, Éric

A2 - Boumerdassi, Selma

A2 - Mühlethaler, Paul

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd International Conference on Machine Learning for Networking, MLN 2020

Y2 - 24 November 2020 through 26 November 2020

ER -

Turhan M, Öztürk E, Çırpan HA. Deep Learning-Aided Spatial Multiplexing with Index Modulation. In Renault É, Boumerdassi S, Mühlethaler P, editörler, Machine Learning for Networking - Third International Conference, MLN 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 226-236. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-70866-5_14

Deep Learning-Aided Spatial Multiplexing with Index Modulation

Özet

Yayın serisi

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Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

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