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Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing (2208.09287v5)

Published 17 Aug 2022 in eess.SP and cs.LG

Abstract: The limited over-the-air (OTA) pilot symbols in multiple-input-multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for detecting transmitted data symbols at the receiver, especially for machine learning-based approaches. While it is crucial to explore effective ways to exploit pilots, one can also take advantage of the data symbols to improve detection performance. Thus, this paper introduces an online attention-based approach, namely RC-AttStructNet-DF, that can efficiently utilize pilot symbols and be dynamically updated with the detected payload data using the decision feedback (DF) mechanism. Reservoir computing (RC) is employed in the time domain network to facilitate efficient online training. The frequency domain network adopts the novel 2D multi-head attention (MHA) module to capture the time and frequency correlations, and the structural-based StructNet to facilitate the DF mechanism. The attention loss is designed to learn the frequency domain network. The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols. The effectiveness of the RC-AttStructNet-DF approach is demonstrated through extensive experiments in MIMO-OFDM and massive MIMO-OFDM systems with different modulation orders and under various scenarios.

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References (31)
  1. 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.
  2. Z. Zhao, M. C. Vuran, F. Guo, and S. D. Scott, “Deep-waveform: A learned OFDM receiver based on deep complex-valued convolutional networks,” IEEE J. Sel. Areas Commun., 2021.
  3. Q. Chen, S. Zhang, S. Xu, and S. Cao, “Efficient MIMO detection with imperfect channel knowledge-a deep learning approach,” in 2019 IEEE Wireless Commun. and Netw. Conf. (WCNC).   IEEE, 2019, pp. 1–6.
  4. M. Honkala, D. Korpi, and J. M. Huttunen, “DeepRx: Fully convolutional deep learning receiver,” IEEE Trans. Wireless Commun., vol. 20, no. 6, pp. 3925–3940, 2021.
  5. X. Lyu, W. Feng, and N. Ge, “Deep neural network-based symbol detection for highly dynamic channels,” in 2020 IEEE Global Commun. Conf. (GLOBECOM), 2020, pp. 1–6.
  6. Y. Liao, N. Farsad, N. Shlezinger, Y. C. Eldar, and A. J. Goldsmith, “Deep neural network symbol detection for millimeter wave communications,” in 2019 IEEE Global Commun. Conf. (GLOBECOM), 2019, pp. 1–6.
  7. X. Yi and C. Zhong, “Deep learning for joint channel estimation and signal detection in OFDM systems,” IEEE Commun. Lett., vol. 24, no. 12, pp. 2780–2784, 2020.
  8. R. Shafin, L. Liu, V. Chandrasekhar, H. Chen, J. Reed, and J. C. Zhang, “Artificial intelligence-enabled cellular networks: A critical path to beyond-5G and 6G,” IEEE Trans. Wireless Commun., vol. 27, no. 2, pp. 212–217, 2020.
  9. L. Liu, R. Chen, S. Geirhofer, K. Sayana, Z. Shi, and Y. Zhou, “Downlink MIMO in LTE-Advanced: SU-MIMO vs. MU-MIMO,” IEEE Commun. Mag., vol. 50, no. 2, pp. 140–147, 2012.
  10. N. Samuel, T. Diskin, and A. Wiesel, “Learning to detect,” IEEE Trans. Signal Process., vol. 67, no. 10, pp. 2554–2564, 2019.
  11. H. He, C.-K. Wen, S. Jin, and G. Y. Li, “A model-driven deep learning network for MIMO detection,” in 2018 IEEE Global Conf. on Signal and Inf. Process. (GlobalSIP).   IEEE, 2018, pp. 584–588.
  12. M. Khani, M. Alizadeh, J. Hoydis, and P. Fleming, “Adaptive neural signal detection for massive MIMO,” IEEE Trans. Wireless Commun., vol. 19, no. 8, pp. 5635–5648, 2020.
  13. H. He, C.-K. 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. S. Shi, Y. Cai, Q. Hu, B. Champagne, and L. Hanzo, “Deep-unfolding neural-network aided hybrid beamforming based on symbol-error probability minimization,” IEEE Trans. Veh. Technol., pp. 1–15, 2022.
  15. K. Kang, Q. Hu, Y. Cai, G. Yu, J. Hoydis, and Y. C. Eldar, “Mixed-timescale deep-unfolding for joint channel estimation and hybrid beamforming,” IEEE J. Sel. Areas Commun., vol. 40, no. 9, pp. 2510–2528, 2022.
  16. H. He, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmWave massive MIMO systems,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, 2018.
  17. “Study on 3D channel model for LTE,” 3GPP TR 36.873., Tech. Rep., 2015.
  18. S. Mosleh, L. Liu, C. Sahin, Y. R. Zheng, and Y. Yi, “Brain-inspired wireless communications: Where reservoir computing meets MIMO-OFDM,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 10, pp. 4694–4708, Oct 2018.
  19. Z. Zhou, L. Liu, and H.-H. Chang, “Learning for detection: MIMO-OFDM symbol detection through downlink pilots,” IEEE Trans. Wireless Commun., vol. 19, no. 6, pp. 3712–3726, 2020.
  20. Z. Zhou, L. Liu, S. Jere, J. C. Zhang, and Y. Yi, “RCNet: incorporating structural information into deep RNN for MIMO-OFDM symbol detection with limited training,” IEEE Trans. Wireless Commun., January 2021.
  21. J. Xu, Z. Zhou, L. Li, L. Zheng, and L. Liu, “RC-Struct: a structure-based neural network approach for MIMO-OFDM detection,” IEEE Trans. Wireless Commun., 2022.
  22. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural inf. process. sys., vol. 30, 2017.
  23. Z. Zhou, L. Liu, V. Chandrasekhar, J. Zhang, and Y. Yi, “Deep reservoir computing meets 5G MIMO-OFDM systems in symbol detection,” in AAAI Conf. on Artificial Intell., vol. 34, no. 01, 2020, pp. 1266–1273.
  24. L. Li, L. Liu, J. C. Zhang, J. D. Ashdown, and Y. Yi, “Reservoir computing meets Wi-Fi in software radios: Neural network-based symbol detection using training sequences and pilots,” in 2020 29th Wireless and Opt. Commun. Conf. (WOCC).   IEEE, 2020, pp. 1–6.
  25. Z. Zhou, S. Jere, L. Zheng, and L. Liu, “Learning for integer-constrained optimization through neural networks with limited training,” in NeurIPS Wkshps on Learn. Meets Combinatorial Algorithms, Dec. 2020.
  26. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conf. on comp. vision and pattern recog., 2016, pp. 770–778.
  27. H. Jaeger, “Adaptive nonlinear system identification with echo state networks,” Advances in Neural Info. Process. Syst., vol. 15, 2002.
  28. A. Ghasemmehdi and E. Agrell, “Faster recursions in sphere decoding,” IEEE Trans. Inf. Theory, vol. 57, no. 6, pp. 3530–3536, 2011.
  29. S. Jaeckel, L. Raschkowski, K. Börner, and L. Thiele, “QuaDRiGa: A 3-D multi-cell channel model with time evolution for enabling virtual field trials,” IEEE Trans. Antennas Propag., vol. 62, no. 6, pp. 3242–3256, 2014.
  30. C. Rapp, “Effects of HPA-nonlinearity on a 4-DPSK/OFDM-signal for a digital sound broadcasting signal,” ESA Special Publication, vol. 332, pp. 179–184, 1991.
  31. E. Karami and M. Shiva, “Decision-directed recursive least squares mimo channels tracking,” EURASIP J. on Wireless Commun. and Netw., vol. 2006, pp. 1–10, 2006.
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