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Soft-decoding for multi-set space-time shift-keying mmWave systems: A deep learning approach

Soft-decoding for multi-set space-time shift-keying mmWave systems: A deep learning approach
Soft-decoding for multi-set space-time shift-keying mmWave systems: A deep learning approach
In this paper, we propose a deep learning assisted soft-demodulator for multi-set space-time shift keying (MS-STSK) millimeter wave (mmWave) systems, where we train a neural network (NN) to provide the soft values of the MS-STSK symbol without relying on explicit channel state information (CSI). Thus, in contrast to the conventional MS-STSK soft-demodulator which relies on the knowledge of CSI at the receiver, the learning-assisted design circumvents the channel estimation while also improving the data rate by dispensing with pilot overhead. Furthermore, our proposed learning-aided soft-demodulation substantially reduces the number of cost-function evaluations at the output of the MS-STSK demodulator. We demonstrate by simulations that despite avoiding CSI-estimation and the pilot overhead, our learning-assisted design performs closely to the channel-estimation aided design assuming perfect CSI for BER < 10−4 , whilst imposing a low complexity. Furthermore, we show by simulations that upon using realistic imperfect CSI at the receiver employing conventional soft-demodulation, the learning-aided soft-demodulator outperforms the conventional scheme. Additionally, we present quantitative discussions on thereceiver complexity in terms of the number of computations required to produce the soft values.
2169-3536
Katla, Satyanarayana
f3436daa-e5da-4b3c-ab4b-ad07a0cef99a
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Mourad, Alain
7a39422e-4c9b-4f76-8653-8551ec8cfb4b
Pietraski, Philip
01164d59-1a3a-4549-816e-a0327fe8b43f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Katla, Satyanarayana
f3436daa-e5da-4b3c-ab4b-ad07a0cef99a
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Mourad, Alain
7a39422e-4c9b-4f76-8653-8551ec8cfb4b
Pietraski, Philip
01164d59-1a3a-4549-816e-a0327fe8b43f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Katla, Satyanarayana, El-Hajjar, Mohammed, Mourad, Alain, Pietraski, Philip and Hanzo, Lajos (2020) Soft-decoding for multi-set space-time shift-keying mmWave systems: A deep learning approach. IEEE Access. (doi:10.1109/ACCESS.2020.2973318).

Record type: Article

Abstract

In this paper, we propose a deep learning assisted soft-demodulator for multi-set space-time shift keying (MS-STSK) millimeter wave (mmWave) systems, where we train a neural network (NN) to provide the soft values of the MS-STSK symbol without relying on explicit channel state information (CSI). Thus, in contrast to the conventional MS-STSK soft-demodulator which relies on the knowledge of CSI at the receiver, the learning-assisted design circumvents the channel estimation while also improving the data rate by dispensing with pilot overhead. Furthermore, our proposed learning-aided soft-demodulation substantially reduces the number of cost-function evaluations at the output of the MS-STSK demodulator. We demonstrate by simulations that despite avoiding CSI-estimation and the pilot overhead, our learning-assisted design performs closely to the channel-estimation aided design assuming perfect CSI for BER < 10−4 , whilst imposing a low complexity. Furthermore, we show by simulations that upon using realistic imperfect CSI at the receiver employing conventional soft-demodulation, the learning-aided soft-demodulator outperforms the conventional scheme. Additionally, we present quantitative discussions on thereceiver complexity in terms of the number of computations required to produce the soft values.

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Accepted/In Press date: 2 February 2020
e-pub ahead of print date: 11 February 2020

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Local EPrints ID: 437827
URI: http://eprints.soton.ac.uk/id/eprint/437827
ISSN: 2169-3536
PURE UUID: f2752136-69c6-4391-ae9f-b469e642e7ca
ORCID for Satyanarayana Katla: ORCID iD orcid.org/0000-0002-5411-3962
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: 19 Feb 2020 17:32
Last modified: 07 Oct 2020 02:01

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Contributors

Author: Satyanarayana Katla ORCID iD
Author: Mohammed El-Hajjar ORCID iD
Author: Alain Mourad
Author: Philip Pietraski
Author: Lajos Hanzo ORCID iD

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