Dergiler / Journal of Naval Sciences and Engineering / 2022 / Cilt: 18 - Sayı: 2

A NEW RECEIVER DESIGN FOR SPATIAL MODULATION SYSTEMS

Sayfa
159–178
Yayın Tarihi
2022-11-30
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

At the same time, even when the data rate is increased, the same DNN structure (without increasing the processing load) shows better/same performance than the receivers in the literature.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In this study, spatial modulation (SM), which is an interesting and new approach for 5G and beyond communication systems, and deep neural network (DNN), which have also received great attention recently, are discussed, and a DNN-based receiver architecture for SM systems is proposed.
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Temel Bulgular & Veri:In this paper, a new DNN-based SM receiver is proposed to detect the transmitted symbols and the activated antenna index at the same time, and its performance is examined.
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Sonuç & Katkı:At the same time, even when the data rate is increased, the same DNN structure (without increasing the processing load) shows better/same performance than the receivers in the literature.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:MIMO · Spatial Modulation · Deep Learning

Abstract

In this study, spatial modulation (SM), which is an interesting and new approach for 5G and beyond communication systems, and deep neural network (DNN), which have also received great attention recently, are discussed, and a DNN-based receiver architecture for SM systems is proposed. Since the DNN will not be retrained until the channel change after training, it requires less processing, so it will be a potential receiver architecture for next-generation wireless communication and therefore SM systems. In this paper, a new DNN-based SM receiver is proposed to detect the transmitted symbols and the activated antenna index at the same time, and its performance is examined. As can be seen from the computer simulations, the DNN-based receiver offers low error performance with a small number of hidden layers and a low number of neurons in these layers. At the same time, even when the data rate is increased, the same DNN structure (without increasing the processing load) shows better/same performance than the receivers in the literature.

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Bu Makaleye Atıf Yap

ALTIN, G. (2022). A NEW RECEIVER DESIGN FOR SPATIAL MODULATION SYSTEMS. Journal of Naval Sciences and Engineering, Cilt: 18 - Sayı: 2, 159–178. https://ajindex.com/articles/860567

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Kaynakça (12)

  1. Albinsaid H., Singh K., Biswas S., Li C. P., Alouini M. S. (2020). “Block deep neural network-based signal detector for generalized spatial modulation”, IEEE Commun. Lett., vol. 24, no. 12, pp. 2775-2779.
  2. Al-Nahhal I., Basar E., Dobre O. A., Ikki S. (2019). “Optimum Low-Complexity Decoder for Spatial Modulation,” IEEE J. Sel. Areas Commun., vol. 37, no. 9, pp. 2001-2013.
  3. Altın G. (2022). “Deep neural network-based detection of index modulated MIMO-OFDM,” Phys. Commun., vol. 52, 101669. Bishop C. M. (2006) Pattern Recognition and Machine Learning, New York, NY, USA: Springer-Verlag. Dai L., Jiao R., Adachi F., Poor V. H., Hanzo L. (2020). “Deep learning for wireless communications: An emerging interdisciplinary paradigm”, IEEE Wireless Commun., vol. 27, no. 4, pp. 133-139.
  4. Hassoun M. (2003) Fundamentals of Artificial Neural Networks, A Bradford Book, Cambridge, MA, USA: MIT Press.
  5. IEEE (2017), IEEE 802.16 Standard: Fixed broadband wireless access systems. IEEE (2020), IEEE 802.11 Standard: Wireless Local Area Networks. Jeganathan J., Ghrayeb A., Szczecinski L. (2008). “Spatial modulation: optimal detection and performance analysis,” IEEE Commun. Lett., vol. 12, no. 8, pp. 545-547.
  6. Jeganathan J., Ghrayeb A., Szczecinski L., Ceron A. (2009). “Space shift keying modulation for MIMO channels”, IEEE Trans. Wireless Commun., vol. 8, no. 7, pp. 3692-3703.
  7. Jiang Y., Wang Y., Wen J., Shao M., Li Y. (2015). “Spatial Modulation Scheme With Low-Complexity Detection Algorithms,” IEEE Commun. Lett., vol. 19, no. 8, pp. 1422-1425.
  8. Kim J., Ro H., Park H. (2021) “Deep learning-based detector for dual mode OFDM with index modulation,” IEEE Wireless Commun. Lett., vol. 10, no. 7, pp. 1562-1566.
  9. Liu T. H., Ye Y. Z., Huang C. K., Chen C. E., Hwang Y. T., Chu Y. S. (2019). “A Low-Complexity Maximum Likelihood Detector for the Spatially Modulated Signals: Algorithm and Hardware Implementation,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 66, no. 11, pp. 1820-1824.
  10. Luong T. V., Ko Y., Vien N. A., Nguyen D. H. N., Matthaiou M. (2019). “Deep learning-based detector for OFDM-IM,” IEEE Wireless Commun. Lett., vol. 8, no. 4, pp. 1159-1162.
  11. Men H., Jin M. (2014). “A Low-Complexity ML Detection Algorithm for Spatial Modulation Systems With M-PSK Constellation,” IEEE Commun. Lett., vol. 18, no. 8, pp. 1375-1378.
  12. Mesleh R., Haas H., Ahn C. W., Yun S. (2006). “Spatial modulation–a new low complexity spectral efficiency enhancing technique,” 2006 First International Conference on Comm