KaydedilenlerDerginizi ekleyin

Dergiler / Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering / 2022 / Cilt: 64 - Sayı: 2

Utilization of deep learning architectures for MIMO detection

Sümeye Nur KARAHAN
Aykut KALAYCIOĞLU
Sayfa
81–94
Yayın Tarihi
2022-12-30
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

For small-scale multiple-input multiple-output systems, the performance of the deep learning based detector is close to that of the optimal detector.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Applications of deep learning in communications systems are becoming popular today with their powerful solutions to complex problems.
📊
Temel Bulgular & Veri:Deep neural network, long short-term memory, and one-dimenisonal convolutional neural network architectures are discussed and the bit error rate performances of these deep learning based detectors are compared with the optimal maximum likelihood and sub-optimal minimum mean square error detectors.
💡
Sonuç & Katkı:For small-scale multiple-input multiple-output systems, the performance of the deep learning based detector is close to that of the optimal detector.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:DNN · LSTM · 1DCNN

Abstract

Applications of deep learning in communications systems are becoming popular today with their powerful solutions to complex problems. This study considers the utilization of deep learning detectors for small-scale multiple-input multiple-output systems. Deep neural network, long short-term memory, and one-dimenisonal convolutional neural network architectures are discussed and the bit error rate performances of these deep learning based detectors are compared with the optimal maximum likelihood and sub-optimal minimum mean square error detectors. Simulation results show that the deep neural network architecture has the best detection performance among the discussed deep learning detectors and may outperform the sub-optimal minimum mean square error detector. For small-scale multiple-input multiple-output systems, the performance of the deep learning based detector is close to that of the optimal detector.

🔖

Bu Makaleye Atıf Yap

KARAHAN, S. N., KALAYCIOĞLU, A. (2022). Utilization of deep learning architectures for MIMO detection. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Cilt: 64 - Sayı: 2, 81–94. https://ajindex.com/articles/858378

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (13)

  1. Yang, S., Hanzo, L., Fifty years of MIMO detection: The road to large-scale MIMOs, IEEE Comm. Surv. Tutor., 17 (4) (2015), 1941-1988, https://doi.org/10.1109/COMST.2015.2475242.
  2. Albreem, M.A., Juntti, M., Shahabuddin, S., Massive MIMO detection techniques: A Survey, IEEE Comm. Surv. Tutor., 21 (4) (2019), 3109-3132, https://doi.org/10.1109/COMST.2019.2935810.
  3. Wei, Y., Zhao, M-M., Hong, M., Zhao, M-J., Lei, M., Learned conjugate gradient descent network for Massive MIMO detection, IEEE Trans. Signal Process., 68 (2020), 6336- 6349, https://doi.org/10.1109/TSP.2020.3035832.
  4. Dai, L., Gao, X., Su, X., Han, S., I, C-L., Wang, Z., Low complexity soft-output signal detection based on Gauss-Seidel method for uplink multiuser large-scale MIMO systems, IEEE Trans. Veh. Technol., 64 (10) (2015), 4839-4845, https://doi.org/10.1109/TVT.2014.2370106.
  5. Yu, A., Jing, S., Tan, X., Wu, Z. Yan, Z., Zhang, Z., You, X., Zhang, C., Efficient successive over relaxation detectors for massive MIMO, IEEE Trans. Circuits Syst. I: Regul. Pap., 67 (6) (2020), 2128-2139, https://doi.org/10.1109/TCSI.2020.2966318.
  6. LeCun, Y., Bengio, Y., Hinton, G., Deep learning, Nature, 521 (2015), 436-444, https://doi.org/10.1038/nature14539.
  7. Zhang, J., He, Y., Li, Y-W., Wen, C-K., Jin, S., Meta learning-based MIMO detectors: Design, simulation, and experimental test, IEEE Trans. Wirel. Commun., 20 (2) (2021), 1122-1137, https://doi.org/10.1109/TWC.2020.3030882.
  8. Albreem, M.A., Alhabbash, A.H., Shahabuddin, S., Juntti, M., Deep learning for massive uplink detectors, IEEE Comm. Surv. Tutor., vol. 24, no. 1, (2022), 741-766, https://doi.org/10.1109/COMST.2021.3135542.
  9. Hershey, J.R., Roux, J.L., Weninger, F., Deep unfolding: Model-based inspiration of novel deep architectures, arXiv: 1409.2574, (2014), https://doi.org/10.48550/arXiv.1409.2574.
  10. Balatsoukas-Stimming, A., Studer, C., Deep unfolding for communication systems: A survey and some new directions, 2019 IEEE International Workshop on Signal Processing Systems (SiPS), (2019), 20-23 October 2019, 266-271,
  11. Nanjing, China, https://doi.org/10.1109/SiPS47522.2019.9020494.
  12. Liao, J., Zhao, J., Gao, F., Li, G.Y., A model-driven deep learning method for massive MIMO detection, IEEE Commun. Lett., vol. 24, no. 8, (2020), 1724-1728, https://doi.org/10.1109/LCOMM.2020.2989672.
  13. He, H., Wen, C-K., Jin, S., L, G.Y., A model-driven deep learning network for MIMO detection, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), (2018), 26-29 No