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Soft-Decoding for Multi-Set Space-Time Shift-Keying mmWave Systems: A Deep Learning Approach
- Source :
- IEEE Access, Vol 8, Pp 49584-49595 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
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 learningassisted design performs closely to the channel-estimation aided design assuming perfect CSI for BER -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 softdemodulator outperforms the conventional scheme. Additionally, we present quantitative discussions on the receiver complexity in terms of the number of computations required to produce the soft values.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.20e36e6eee3845c7a80009d349a46340
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2020.2973318