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Deep learning assisted detection for index modulation aided mmwave systems

Deep learning assisted detection for index modulation aided mmwave systems
Deep learning assisted detection for index modulation aided mmwave systems
In this paper, we propose deep learning assisted detection for index modulation millimeter wave (mmWave) systems, where we train a neural network (NN) to jointly detect the transmitted data and index information without relying on explicit channel state information (CSI). As a design example, we first employ multi-set space-time shift keying (MS-STSK) combined with beamforming for transmission over the mmWave channel, where the information is conveyed implicitly using the index of the antennas, the dispersion matrix and the M-ary constellation. Then, we analyze our design when MS-STSK transmission is considered in conjunction with beam index modulation (BIM), where the information is also conveyed by the beam index in addition to the MS-STSK information. In contrast to the MS-STSK’s conventional maximum likelihood (ML) detector, our learning-assisted detection dispenses with the channel estimation stage. We demonstrate by simulations that the learning assisted detection outperforms the ML-aided detection in the face of channel impairments with low complexity. Furthermore, we show by simulations that ML-aided detection produces an error floor, when the MS-STSK transmission
is coupled with BIM, when realistic channel estimation errors are considered. Additionally, we present qualitative discussions on the receiver complexity in terms of its search space as well as the number of computations required.
2169-3536
1-18
Katla, Satyanarayana
f3436daa-e5da-4b3c-ab4b-ad07a0cef99a
Xiang, Luping
56d951c0-455e-4a67-b167-f6c8233343b1
Zhang, Yanqing
b1cd478d-ba8c-4063-8a12-2b67945cee03
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Mourad, Alain
7a39422e-4c9b-4f76-8653-8551ec8cfb4b
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Katla, Satyanarayana
f3436daa-e5da-4b3c-ab4b-ad07a0cef99a
Xiang, Luping
56d951c0-455e-4a67-b167-f6c8233343b1
Zhang, Yanqing
b1cd478d-ba8c-4063-8a12-2b67945cee03
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Mourad, Alain
7a39422e-4c9b-4f76-8653-8551ec8cfb4b
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Katla, Satyanarayana, Xiang, Luping, Zhang, Yanqing, El-Hajjar, Mohammed, Mourad, Alain and Hanzo, Lajos (2020) Deep learning assisted detection for index modulation aided mmwave systems. IEEE Access, 4, 1-18. (In Press)

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Abstract

In this paper, we propose deep learning assisted detection for index modulation millimeter wave (mmWave) systems, where we train a neural network (NN) to jointly detect the transmitted data and index information without relying on explicit channel state information (CSI). As a design example, we first employ multi-set space-time shift keying (MS-STSK) combined with beamforming for transmission over the mmWave channel, where the information is conveyed implicitly using the index of the antennas, the dispersion matrix and the M-ary constellation. Then, we analyze our design when MS-STSK transmission is considered in conjunction with beam index modulation (BIM), where the information is also conveyed by the beam index in addition to the MS-STSK information. In contrast to the MS-STSK’s conventional maximum likelihood (ML) detector, our learning-assisted detection dispenses with the channel estimation stage. We demonstrate by simulations that the learning assisted detection outperforms the ML-aided detection in the face of channel impairments with low complexity. Furthermore, we show by simulations that ML-aided detection produces an error floor, when the MS-STSK transmission
is coupled with BIM, when realistic channel estimation errors are considered. Additionally, we present qualitative discussions on the receiver complexity in terms of its search space as well as the number of computations required.

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Accepted/In Press date: 2 November 2020

Identifiers

Local EPrints ID: 444915
URI: http://eprints.soton.ac.uk/id/eprint/444915
ISSN: 2169-3536
PURE UUID: 354606ea-4f6b-4f7a-89ae-eca395a5ba26
ORCID for Satyanarayana Katla: ORCID iD orcid.org/0000-0002-5411-3962
ORCID for Luping Xiang: ORCID iD orcid.org/0000-0003-1465-6708
ORCID for Yanqing Zhang: ORCID iD orcid.org/0000-0003-2349-1925
ORCID for Mohammed El-Hajjar: ORCID iD orcid.org/0000-0002-7987-1401
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 11 Nov 2020 17:31
Last modified: 18 Feb 2021 17:40

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Contributors

Author: Satyanarayana Katla ORCID iD
Author: Luping Xiang ORCID iD
Author: Yanqing Zhang ORCID iD
Author: Mohammed El-Hajjar ORCID iD
Author: Alain Mourad
Author: Lajos Hanzo ORCID iD

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