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.
Katla, Satyanarayana
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El-Hajjar, Mohammed
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Mourad, Alain
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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).
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
Identifiers
Local EPrints ID: 437827
URI: http://eprints.soton.ac.uk/id/eprint/437827
ISSN: 2169-3536
PURE UUID: f2752136-69c6-4391-ae9f-b469e642e7ca
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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
Author:
Mohammed El-Hajjar
Author:
Alain Mourad
Author:
Philip Pietraski
Author:
Lajos Hanzo
University divisions
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