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Truncated Polynomial Expansion-Based Detection in Massive MIMO: A Model-Driven Deep Learning Approach

Published 19 Feb 2024 in eess.SP and cs.LG | (2402.12595v1)

Abstract: In this paper, we propose a deep learning (DL)-based approach for efficiently computing the inverse of Hermitian matrices using truncated polynomial expansion (TPE). Our model-driven approach involves optimizing the coefficients of the TPE during an offline training procedure for a given number of TPE terms. We apply this method to signal detection in uplink massive multiple-input multiple-output (MIMO) systems, where the matrix inverse operation required by linear detectors, such as zero-forcing (ZF) and minimum mean square error (MMSE), is approximated using TPE. Our simulation results demonstrate that the proposed learned TPE-based method outperforms the conventional TPE method with optimal coefficients in terms of asymptotic convergence speed and reduces the computational complexity of the online detection stage, albeit at the expense of the offline training stage. However, the limited number of trainable parameters leads to a swift offline training process.

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References (21)
  1. T. Wang, C. K. Wen, H. Wang, F. Gao, T. Jiang, and S. Jin, “Deep learning for wireless physical layer: Opportunities and challenges,” China Commun., vol. 14, pp. 92–111, 2017.
  2. S. Dörner, S. Cammerer, J. Hoydis, and S. t. Brink, “Deep learning based communication over the air,” IEEE J. Sel. Topics Signal Process., vol. 12, no. 1, pp. 132–143, 2018.
  3. Z. Qin, H. Ye, G. Y. Li, and B. F. Juang, “Deep learning in physical layer communications,” IEEE Wireless Commun., vol. 26, no. 2, pp. 93–99, 2019.
  4. H. He, S. Jin, C. Wen, F. Gao, G. Y. Li, and Z. Xu, “Model-driven deep learning for physical layer communications,” IEEE Wireless Commun., vol. 26, no. 5, pp. 77–83, 2019.
  5. H. Ye, G. Y. Li, and B. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” IEEE Wireless Commun. Lett., vol. 7, no. 1, pp. 114–117, 2018.
  6. Y. Wu, X. Li, and J. Fang, “A deep learning approach for modulation recognition via exploiting temporal correlations,” in IEEE SPAWC, 2018, pp. 1–5.
  7. C. Wen, W. Shih, and S. Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 748–751, 2018.
  8. Y. Wei, M. Zhao, M. Hong, M. Zhao, and M. Lei, “Learned conjugate gradient descent network for massive MIMO detection,” in IEEE ICC, 2020, pp. 1–6.
  9. J. R. Hershey, J. L. Roux, and F. Weninger, “Deep unfolding: Model-based inspiration of novel deep architectures,” in Proc. NIPS, 2014.
  10. M. Borgerding, P. Schniter, and S. Rangan, “AMP-inspired deep networks for sparse linear inverse problems,” IEEE Trans, Signal Process., vol. 65, no. 16, pp. 4293–4308, 2017.
  11. N. Samuel, T. Diskin, and A. Wiesel, “Deep MIMO detection,” in Proc. IEEE SPAWC, July 2017, pp. 1–5.
  12. N. Samuel, T. Diskin, and A. Wiesel, “Learning to detect,” IEEE Trans. Signal Process., vol. 67, no. 10, pp. 2554–2564, 2019.
  13. H. He, C. Wen, S. Jin, and G. Y. Li, “Model-driven deep learning for MIMO detection,” IEEE Trans. Signal Process., vol. 68, pp. 1702–1715, 2020.
  14. X. Tan, W. Xu, K. Sun, Y. Xu, Y. Be’ery, X. You, and C. Zhang, “Improving massive MIMO message passing detectors with deep neural network,” IEEE Trans. Veh. Technol., vol. 69, no. 2, pp. 1267–1280, 2020.
  15. M. Mandloi and V. Bhatia, “Low-complexity near-optimal iterative sequential detection for uplink massive MIMO systems,” IEEE Commun. Lett., vol. 21, no. 3, pp. 568–571, 2017.
  16. J. Liao, J. Zhao, F. Gao, and G. Y. Li, “A model-driven deep learning method for massive MIMO detection,” IEEE Commun. Lett., vol. 24, no. 8, pp. 1724–1728, 2020.
  17. G. M. A. Sessler and F. K. Jondral, “Rapidly converging polynomial expansion multiuser detector with low complexity for CDMA systems,” Electron. Lett., vol. 38, no. 17, 2002.
  18. K. Izadinasab, A. W. Shaban, and O. Damen, “Low-complexity detectors for uplink massive MIMO systems leveraging truncated polynomial expansion,” IEEE Access, vol. 10, 2022.
  19. G. M. A. Sessler and F. K. Jondral, “Low-complexity polynomial expansion multiuser detector for CDMA systems,” IEEE Trans. Veh. Technol., vol. 54, no. 4, pp. 1379–1391, 2005.
  20. A. Müller, A. Kammoun, E. Björnson, and M. Debbah, “Efficient linear precoding for massive MIMO systems using truncated polynomial expansion,” in Proc. IEEE Sensor Array and Multichannel Signal Process. Workshop (SAM), 2014, pp. 273–276.
  21. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in ICLR, 2015.

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