Utilization of deep learning architectures for MIMO detection

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.

Keywords

• DNN
• LSTM
• 1DCNN
• MIMO
• deep learning
• detection

References

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, Nanjing, China, https://doi.org/10.1109/SiPS47522.2019.9020494.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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