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2D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detection (2311.08543v2)

Published 14 Nov 2023 in eess.SP, cs.AI, and cs.LG

Abstract: Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS system, where only a limited number of over-the-air (OTA) pilot symbols are utilized for training. However, this approach does not leverage the domain knowledge specific to the OTFS system to fully unlock the potential of RC. This paper introduces a novel two-dimensional RC (2D-RC) method that incorporates the domain knowledge of the OTFS system into the design for symbol detection in an online subframe-based manner. Specifically, as the channel interaction in the delay-Doppler (DD) domain is a two-dimensional (2D) circular operation, the 2D-RC is designed to have the 2D circular padding procedure and the 2D filtering structure to embed this knowledge. With the introduced architecture, 2D-RC can operate in the DD domain with only a single neural network, instead of necessitating multiple RCs to track channel variations in the time domain as in previous work. Numerical experiments demonstrate the advantages of the 2D-RC approach over the previous RC-based approach and compared model-based methods across different OTFS system variants and modulation orders.

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References (49)
  1. M. Series, “IMT Vision–Framework and overall objectives of the future development of IMT for 2020 and beyond,” Recommendation ITU, vol. 2083, no. 0, 2015.
  2. 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 Wireless Commun., vol. 27, no. 2, pp. 212–217, 2020.
  3. R. Hadani, S. Rakib, M. Tsatsanis, A. Monk, A. J. Goldsmith, A. F. Molisch, and R. Calderbank, “Orthogonal time frequency space modulation,” in 2017 IEEE Wireless Commun. Netw. Conf., 2017, pp. 1–6.
  4. Z. Wei, W. Yuan, S. Li, J. Yuan, G. Bharatula, R. Hadani, and L. Hanzo, “Orthogonal time-frequency space modulation: A promising next-generation waveform,” IEEE Wireless Commun., vol. 28, no. 4, pp. 136–144, 2021.
  5. W. Yuan, S. Li, Z. Wei, Y. Cui, J. Jiang, H. Zhang, and P. Fan, “New delay doppler communication paradigm in 6g era: A survey of orthogonal time frequency space (OTFS),” China Commun., vol. 20, no. 6, pp. 1–25, 2023.
  6. S. K. Mohammed, R. Hadani, A. Chockalingam, and R. Calderbank, “OTFS—A mathematical foundation for communication and radar sensing in the delay-Doppler domain,” IEEE BITS the Inf. Theory Mag., vol. 2, no. 2, pp. 36–55, 2022.
  7. G. Surabhi and A. Chockalingam, “Low-complexity linear equalization for OTFS modulation,” IEEE Commun. Lett., vol. 24, no. 2, pp. 330–334, 2019.
  8. S. Tiwari, S. S. Das, and V. Rangamgari, “Low complexity LMMSE receiver for OTFS,” IEEE Commun. Lett., vol. 23, no. 12, pp. 2205–2209, 2019.
  9. T. Zou, W. Xu, H. Gao, Z. Bie, Z. Feng, and Z. Ding, “Low-complexity linear equalization for OTFS systems with rectangular waveforms,” in 2021 IEEE Intl. Conf. on Commun. (ICC).   IEEE, 2021, pp. 1–6.
  10. P. Raviteja, K. T. Phan, Y. Hong, and E. Viterbo, “Interference cancellation and iterative detection for orthogonal time frequency space modulation,” IEEE Trans. Wireless Commun., vol. 17, no. 10, pp. 6501–6515, 2018.
  11. Z. Yuan, F. Liu, W. Yuan, Q. Guo, Z. Wang, and J. Yuan, “Iterative detection for orthogonal time frequency space modulation with unitary approximate message passing,” IEEE Trans. Wireless Commun., vol. 21, no. 2, pp. 714–725, 2021.
  12. F. Liu, Z. Yuan, Q. Guo, Z. Wang, and P. Sun, “Message passing-based structured sparse signal recovery for estimation of OTFS channels with fractional Doppler shifts,” IEEE Trans. Wireless Commun., vol. 20, no. 12, pp. 7773–7785, 2021.
  13. T. Thaj and E. Viterbo, “Low complexity iterative rake decision feedback equalizer for zero-padded OTFS systems,” IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 15 606–15 622, 2020.
  14. H. Zhang and T. Zhang, “A low-complexity message passing detector for OTFS modulation with probability clipping,” IEEE Wireless Commun. Lett., vol. 10, no. 6, pp. 1271–1275, 2021.
  15. W. Yuan, Z. Wei, J. Yuan, and D. W. K. Ng, “A simple variational Bayes detector for orthogonal time frequency space (OTFS) modulation,” IEEE Trans. Veh. Technol., vol. 69, no. 7, pp. 7976–7980, 2020.
  16. H. Qu, G. Liu, L. Zhang, S. Wen, and M. A. Imran, “Low-complexity symbol detection and interference cancellation for OTFS system,” IEEE Trans. Commun., vol. 69, no. 3, pp. 1524–1537, 2021.
  17. Y. Shan, F. Wang, and Y. Hao, “Orthogonal time frequency space detection via low-complexity expectation propagation,” IEEE Trans. Wireless Commun., vol. 21, no. 12, pp. 10 887–10 901, 2022.
  18. S. Li, W. Yuan, Z. Wei, and J. Yuan, “Cross domain iterative detection for orthogonal time frequency space modulation,” IEEE Trans. Wireless Commun., vol. 21, no. 4, pp. 2227–2242, 2021.
  19. S. Li, W. Yuan, Z. Wei, J. Yuan, B. Bai, D. W. K. Ng, and Y. Xie, “Hybrid MAP and PIC detection for OTFS modulation,” IEEE Trans. Veh. Technol., vol. 70, no. 7, pp. 7193–7198, 2021.
  20. Y. K. Enku, B. Bai, F. Wan, C. U. Guyo, I. N. Tiba, C. Zhang, and S. Li, “Two-dimensional convolutional neural network-based signal detection for OTFS systems,” IEEE Wireless Commun. Lett., vol. 10, no. 11, pp. 2514–2518, 2021.
  21. A. Naikoti and A. Chockalingam, “Low-complexity delay-Doppler symbol DNN for OTFS signal detection,” in 2021 IEEE 93rd Veh. Technol. Conf. (VTC2021-Spring).   IEEE, 2021, pp. 1–6.
  22. Y. K. Enku, B. Bai, S. Li, M. Liu, and I. N. Tiba, “Deep-learning based signal detection for MIMO-OTFS systems,” in 2022 IEEE Intl. Conf. on Commun. (ICC).   IEEE, 2022, pp. 1–5.
  23. X. Zhang, L. Xiao, S. Li, Q. Yuan, L. Xiang, and T. Jiang, “Gaussian AMP aided model-driven learning for OTFS system,” IEEE Commun. Lett., vol. 26, no. 12, pp. 2949–2953, 2022.
  24. X. Zhang, S. Zhang, L. Xiao, S. Li, and T. Jiang, “Graph neural network assisted efficient signal detection for OTFS systems,” IEEE Commun. Lett., 2023.
  25. 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 Wireless Commun., vol. 27, no. 2, pp. 212–217, 2020.
  26. 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.
  27. Z. Zhou, L. Liu, J. Xu, and R. Calderbank, “Learning to equalize OTFS,” IEEE Trans. Wireless Commun., vol. 21, no. 9, pp. 7723–7736, 2022.
  28. 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.
  29. G. Tanaka, T. Yamane, J. B. Héroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose, “Recent advances in physical reservoir computing: A review,” Neural Netw., vol. 115, pp. 100–123, 2019.
  30. A. Jalalvand, G. Van Wallendael, and R. Van de Walle, “Real-time reservoir computing network-based systems for detection tasks on visual contents,” in 2015 7th Intl. Conf. on Comp. Intell., Commun. Syst. and Netw.   IEEE, 2015, pp. 146–151.
  31. Z. Tong and G. Tanaka, “Reservoir computing with untrained convolutional neural networks for image recognition,” in 2018 24th Intl. Conf. on Pattern Recognition (ICPR).   IEEE, 2018, pp. 1289–1294.
  32. F. Triefenbach, A. Jalalvand, B. Schrauwen, and J.-P. Martens, “Phoneme recognition with large hierarchical reservoirs,” Advances in neural info. process. syst., vol. 23, pp. 2307–2315, 2010.
  33. D. Verstraeten, B. Schrauwen, and D. Stroobandt, “Reservoir-based techniques for speech recognition,” in The 2006 IEEE Intl. Joint Conf. on Neural Netw. Proceedings.   IEEE, 2006, pp. 1050–1053.
  34. 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.
  35. Z. Zhou, L. Liu, S. Jere, J. Zhang, and Y. Yi, “RCNet: incorporating structural information into deep RNN for online MIMO-OFDM symbol detection with limited training,” IEEE Trans. Wireless Commun., vol. 20, no. 6, pp. 3524–3537, 2021.
  36. 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., vol. 21, no. 9, pp. 7181–7193, 2022.
  37. J. Xu, L. Li, L. Zheng, and L. Liu, “Detect to learn: Structure learning with attention and decision feedback for MIMO-OFDM receive processing,” IEEE Trans. Commun., pp. 1–1, 2023.
  38. L. Li, J. Xu, L. Zheng, and L. Liu, “Real-time machine learning for multi-user massive MIMO: Symbol detection using Multi-Mode StructNet,” IEEE Trans. Wireless Commun., pp. 1–1, 2023.
  39. M. Lukoševičius and H. Jaeger, “Reservoir computing approaches to recurrent neural network training,” Comput. Sci. Review, vol. 3, no. 3, pp. 127–149, 2009.
  40. H. Jaeger, “The “echo state” approach to analysing and training recurrent neural networks-with an erratum note,” Bonn, Germany: German National Research Center for Inf. Technol. GMD Technical Report, vol. 148, no. 34, p. 13, 2001.
  41. M. Lukoševičius, “A practical guide to applying echo state networks,” in Neural netw.: Tricks of the trade.   Springer, 2012, pp. 659–686.
  42. P. Raviteja, Y. Hong, E. Viterbo, and E. Biglieri, “Practical pulse-shaping waveforms for reduced-cyclic-prefix OTFS,” IEEE Trans. Veh. Technol., vol. 68, no. 1, pp. 957–961, 2018.
  43. P. Raviteja, K. T. Phan, and Y. Hong, “Embedded pilot-aided channel estimation for OTFS in delay–Doppler channels,” IEEE Trans. Veh. Technol., vol. 68, no. 5, pp. 4906–4917, 2019.
  44. W. Yuan, S. Li, Z. Wei, J. Yuan, and D. W. K. Ng, “Data-aided channel estimation for OTFS systems with a superimposed pilot and data transmission scheme,” IEEE Wireless Commun. Lett., vol. 10, no. 9, pp. 1954–1958, 2021.
  45. A. Veit, M. J. Wilber, and S. Belongie, “Residual networks behave like ensembles of relatively shallow networks,” Advances in neural info. process. syst., vol. 29, 2016.
  46. P. Hoeher, S. Kaiser, and P. Robertson, “Two-dimensional pilot-symbol-aided channel estimation by wiener filtering,” in 1997 IEEE int. conf. on acoust., speech, and signal process., vol. 3.   IEEE, 1997, pp. 1845–1848.
  47. Y. Rahmatallah and S. Mohan, “Peak-to-average power ratio reduction in OFDM systems: A survey and taxonomy,” IEEE Commun. Surveys Tuts., vol. 15, no. 4, pp. 1567–1592, 2013.
  48. A. Thomas, K. Deka, P. Raviteja, and S. Sharma, “Convolutional sparse coding based channel estimation for OTFS-SCMA in uplink,” IEEE Trans. Commun., vol. 70, no. 8, pp. 5241–5257, 2022.
  49. S. S. Das, V. Rangamgari, S. Tiwari, and S. C. Mondal, “Time domain channel estimation and equalization of CP-OTFS under multiple fractional dopplers and residual synchronization errors,” IEEE Access, vol. 9, pp. 10 561–10 576, 2020.

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