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Reduced complexity learning-assisted joint channel estimation and detection of compressed sensing-aided multi-dimensional index modulation

Reduced complexity learning-assisted joint channel estimation and detection of compressed sensing-aided multi-dimensional index modulation
Reduced complexity learning-assisted joint channel estimation and detection of compressed sensing-aided multi-dimensional index modulation
Index Modulation (IM) is a flexible transmission scheme capable of striking a flexible performance, throughput, diversity and complexity trade-off. The concept of Multidimensional IM (MIM) has been developed to combine the benefits of IM in multiple dimensions, such as space and frequency. Furthermore, Compressed Sensing (CS) can be beneficially combined with IM in order to increase its throughput. However, having accurate Channel State Information (CSI) is essential for reliable MIM, which requires high pilot overhead. Hence, Joint Channel Estimation and Detection (JCED) is harnessed to reduce the pilot overhead and improve the detection performance at a modestly increased estimation complexity. We then circumvent this by proposing Deep Learning (DL) based JCED for CS aided MIM (CS-MIM) of significantly reducing the complexity, despite reducing the pilot overhead needed for Channel Estimation (CE). Furthermore, we conceive training-aided Soft-Decision (SD) detection. We first analyze the complexity of the conventional joint CE and SD detection followed by proposing our reduced-complexity learning-aided joint CE and SD detection. Our simulation results confirm a Deep Neural Network (DNN) is capable of near-capacity JCED of CS-MIM at a reduced pilot overhead and reduced complexity both for Hard-Decision (HD) and SD detection.
2644-1330
Feng, Xinyu
c4e07886-1f4d-4933-89be-d373c5bd437d
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Feng, Xinyu
c4e07886-1f4d-4933-89be-d373c5bd437d
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Feng, Xinyu, El-Hajjar, Mohammed, Xu, Chao and Hanzo, Lajos (2023) Reduced complexity learning-assisted joint channel estimation and detection of compressed sensing-aided multi-dimensional index modulation. IEEE Open Journal of Vehicular Technology. (In Press)

Record type: Article

Abstract

Index Modulation (IM) is a flexible transmission scheme capable of striking a flexible performance, throughput, diversity and complexity trade-off. The concept of Multidimensional IM (MIM) has been developed to combine the benefits of IM in multiple dimensions, such as space and frequency. Furthermore, Compressed Sensing (CS) can be beneficially combined with IM in order to increase its throughput. However, having accurate Channel State Information (CSI) is essential for reliable MIM, which requires high pilot overhead. Hence, Joint Channel Estimation and Detection (JCED) is harnessed to reduce the pilot overhead and improve the detection performance at a modestly increased estimation complexity. We then circumvent this by proposing Deep Learning (DL) based JCED for CS aided MIM (CS-MIM) of significantly reducing the complexity, despite reducing the pilot overhead needed for Channel Estimation (CE). Furthermore, we conceive training-aided Soft-Decision (SD) detection. We first analyze the complexity of the conventional joint CE and SD detection followed by proposing our reduced-complexity learning-aided joint CE and SD detection. Our simulation results confirm a Deep Neural Network (DNN) is capable of near-capacity JCED of CS-MIM at a reduced pilot overhead and reduced complexity both for Hard-Decision (HD) and SD detection.

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Paper_2__oct - Accepted Manuscript
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Accepted/In Press date: 17 November 2023

Identifiers

Local EPrints ID: 484686
URI: http://eprints.soton.ac.uk/id/eprint/484686
ISSN: 2644-1330
PURE UUID: 0acc5e82-6a10-4861-a170-81441e4632f7
ORCID for Xinyu Feng: ORCID iD orcid.org/0009-0006-8363-4771
ORCID for Mohammed El-Hajjar: ORCID iD orcid.org/0000-0002-7987-1401
ORCID for Chao Xu: ORCID iD orcid.org/0000-0002-8423-0342
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 20 Nov 2023 17:42
Last modified: 14 May 2024 01:58

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Contributors

Author: Xinyu Feng ORCID iD
Author: Mohammed El-Hajjar ORCID iD
Author: Chao Xu ORCID iD
Author: Lajos Hanzo ORCID iD

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