Research Article
BibTex RIS Cite

Utilization of deep learning architectures for MIMO detection

Year 2022, Volume: 64 Issue: 2, 81 - 94, 30.12.2022
https://doi.org/10.33769/aupse.1140193

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.

Supporting Institution

--

Project Number

--

References

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • LeCun, Y., Bengio, Y., Hinton, G., Deep learning, Nature, 521 (2015), 436-444, https://doi.org/10.1038/nature14539.
  • 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.
  • 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.
  • 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.
  • 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, Nanjing, China, https://doi.org/10.1109/SiPS47522.2019.9020494.
  • 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.
  • 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 November 2018, 584-588, Anaheim, CA, USA, https://doi.org/10.1109/GlobalSIP.2018.8646357.
  • O’Shea, T.J., Erpek, T., Clancy, T.C., Deep learning-based MIMO communications, arXiv: 1707.07980, (2017), https://doi.org/10.48550/arXiv.1707.07980.
  • Samuel N., Diskin, T., Wiesel, A., Learning to detect, IEEE Trans. Signal Process., 67 (10), (2019), 2554-2564, https://doi.org/10.1109/TSP.2019.2899805.
  • Baek, M-S., Kwak, S., Jung, J-Y., Kim, H.M., Choi, D-J., Implementation methodologies of deep learning-based signal detection for conventional MIMO transmitters, IEEE Trans. Broadcast., 65(3), (2019), 636-642, https://doi.org/10.1109/TBC.2019.2891051.
  • Chen, Q., Zhang, S., Xu, S., Cao, S., Efficient MIMO detection with imperfect channel knowledge – A deep learning approach, 2019 IEEE Wireless Communications and Networking Conference (WCNC), (2019), 15-18 April 2019, 1-6, Marakkesh, Morocco, https://doi.org/10.1109/WCNC.2019.8885582.
  • Corlay, V., Boutros, J.J., Ciblat, P., Brunel, L., Multilevel MIMO detection with deep learning, 2018 52nd Asilomar Conference on Signals, Systems, and Computers, (2018), 28-31 October 2018, 1805-1809, Pacific Grove, CA, USA, https://doi.org/10.1109/ACSSC.2018.8645519.
  • Poudel, B., Oshima, J., Kobayashi, H., Iwashita, K., MIMO detection using a deep learning neural network in a mode division multiplexing optical transmission system, Opt. Commun., 440 (2019), 41-48, https://doi.org/10.1016/j.optcom.2019.02.016.
  • Lin, C., Chang, Q., Li, X., A deep learning approach for MIMO-NOMA downlink signal detection, Sensors, 19 (2526) (2019), https://doi.org/10.3390/s19112526.
  • Sun, J., Zhang, Y., Xue, J., Xu, Z., Learning to search for MIMO detection, IEEE Trans. Wirel. Commun., 19 (11) (2020), 7571-7584, https://doi.org/10.1109/TWC.2020.3012785.
  • Zappone, A., Renzo, M.D., Debbah, M., Wireless network design in the era of deep learning: Model-based, AI-based, or both?, IEEE Trans. Commun., 67 (10) (2019), 7331- 7376, https://doi.org/10.1109/TCOMM.2019.2924010
Year 2022, Volume: 64 Issue: 2, 81 - 94, 30.12.2022
https://doi.org/10.33769/aupse.1140193

Abstract

Project Number

--

References

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • LeCun, Y., Bengio, Y., Hinton, G., Deep learning, Nature, 521 (2015), 436-444, https://doi.org/10.1038/nature14539.
  • 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.
  • 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.
  • 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.
  • 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, Nanjing, China, https://doi.org/10.1109/SiPS47522.2019.9020494.
  • 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.
  • 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 November 2018, 584-588, Anaheim, CA, USA, https://doi.org/10.1109/GlobalSIP.2018.8646357.
  • O’Shea, T.J., Erpek, T., Clancy, T.C., Deep learning-based MIMO communications, arXiv: 1707.07980, (2017), https://doi.org/10.48550/arXiv.1707.07980.
  • Samuel N., Diskin, T., Wiesel, A., Learning to detect, IEEE Trans. Signal Process., 67 (10), (2019), 2554-2564, https://doi.org/10.1109/TSP.2019.2899805.
  • Baek, M-S., Kwak, S., Jung, J-Y., Kim, H.M., Choi, D-J., Implementation methodologies of deep learning-based signal detection for conventional MIMO transmitters, IEEE Trans. Broadcast., 65(3), (2019), 636-642, https://doi.org/10.1109/TBC.2019.2891051.
  • Chen, Q., Zhang, S., Xu, S., Cao, S., Efficient MIMO detection with imperfect channel knowledge – A deep learning approach, 2019 IEEE Wireless Communications and Networking Conference (WCNC), (2019), 15-18 April 2019, 1-6, Marakkesh, Morocco, https://doi.org/10.1109/WCNC.2019.8885582.
  • Corlay, V., Boutros, J.J., Ciblat, P., Brunel, L., Multilevel MIMO detection with deep learning, 2018 52nd Asilomar Conference on Signals, Systems, and Computers, (2018), 28-31 October 2018, 1805-1809, Pacific Grove, CA, USA, https://doi.org/10.1109/ACSSC.2018.8645519.
  • Poudel, B., Oshima, J., Kobayashi, H., Iwashita, K., MIMO detection using a deep learning neural network in a mode division multiplexing optical transmission system, Opt. Commun., 440 (2019), 41-48, https://doi.org/10.1016/j.optcom.2019.02.016.
  • Lin, C., Chang, Q., Li, X., A deep learning approach for MIMO-NOMA downlink signal detection, Sensors, 19 (2526) (2019), https://doi.org/10.3390/s19112526.
  • Sun, J., Zhang, Y., Xue, J., Xu, Z., Learning to search for MIMO detection, IEEE Trans. Wirel. Commun., 19 (11) (2020), 7571-7584, https://doi.org/10.1109/TWC.2020.3012785.
  • Zappone, A., Renzo, M.D., Debbah, M., Wireless network design in the era of deep learning: Model-based, AI-based, or both?, IEEE Trans. Commun., 67 (10) (2019), 7331- 7376, https://doi.org/10.1109/TCOMM.2019.2924010
There are 21 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Sümeye Nur Karahan 0000-0002-7856-7053

Aykut Kalaycıoğlu 0000-0001-8291-9958

Project Number --
Publication Date December 30, 2022
Submission Date July 4, 2022
Acceptance Date August 2, 2022
Published in Issue Year 2022 Volume: 64 Issue: 2

Cite

APA Karahan, S. N., & Kalaycıoğlu, A. (2022). Utilization of deep learning architectures for MIMO detection. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 64(2), 81-94. https://doi.org/10.33769/aupse.1140193
AMA Karahan SN, Kalaycıoğlu A. Utilization of deep learning architectures for MIMO detection. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. December 2022;64(2):81-94. doi:10.33769/aupse.1140193
Chicago Karahan, Sümeye Nur, and Aykut Kalaycıoğlu. “Utilization of Deep Learning Architectures for MIMO Detection”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 64, no. 2 (December 2022): 81-94. https://doi.org/10.33769/aupse.1140193.
EndNote Karahan SN, Kalaycıoğlu A (December 1, 2022) Utilization of deep learning architectures for MIMO detection. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 64 2 81–94.
IEEE S. N. Karahan and A. Kalaycıoğlu, “Utilization of deep learning architectures for MIMO detection”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 64, no. 2, pp. 81–94, 2022, doi: 10.33769/aupse.1140193.
ISNAD Karahan, Sümeye Nur - Kalaycıoğlu, Aykut. “Utilization of Deep Learning Architectures for MIMO Detection”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 64/2 (December 2022), 81-94. https://doi.org/10.33769/aupse.1140193.
JAMA Karahan SN, Kalaycıoğlu A. Utilization of deep learning architectures for MIMO detection. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2022;64:81–94.
MLA Karahan, Sümeye Nur and Aykut Kalaycıoğlu. “Utilization of Deep Learning Architectures for MIMO Detection”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 64, no. 2, 2022, pp. 81-94, doi:10.33769/aupse.1140193.
Vancouver Karahan SN, Kalaycıoğlu A. Utilization of deep learning architectures for MIMO detection. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2022;64(2):81-94.

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.