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Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques

Published 18 Sep 2023 in eess.SP, cs.IT, and math.IT | (2309.09575v3)

Abstract: Massive multiple-input multiple-output (MIMO) has been a critical enabling technology in 5th generation (5G) wireless networks. With the advent of 6G, a natural evolution is to employ even more antennas, potentially an order of magnitude more, to meet the ever-increasing demand for spectral efficiency. This is beyond a mere quantitative scale-up. The enlarged array aperture brings a paradigm shift towards near-field communications, departing from traditional far-field approaches. However, designing advanced transceiver algorithms for near-field systems is extremely challenging because of the enormous system scale, the complicated channel characteristics, and the uncertainties in the propagation environments. Hence, it is important to develop scalable, low-complexity, and robust algorithms that can efficiently characterize and leverage the properties of the near-field channel. In this article, we discuss the principles and advocate two general frameworks to design deep learning-based near-field transceivers covering both iterative and non-iterative algorithms. Case studies on channel estimation and beam focusing are presented to provide a hands-on tutorial. Finally, we discuss open issues and shed light on future directions.

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References (15)
  1. K. B. Letaief, Y. Shi, J. Lu, and J. Lu, “Edge artificial intelligence for 6G: Vision, enabling technologies, and applications,” IEEE J. Sel. Areas Commun., vol. 40, no. 1, pp. 5–36, Jan. 2022.
  2. Z. Wang, J. Zhang, H. Du, D. Niyato, S. Cui, B. Ai, M. Debbah, K. B. Letaief, and H. V. Poor, “A tutorial on extremely large-scale MIMO for 6G: Fundamentals, signal processing, and applications,” arXiv preprint arXiv:2307.07340, 2023.
  3. W. Tang, M. Z. Chen, J. Y. Dai, Y. Zeng, X. Zhao, S. Jin, Q. Cheng, and T. J. Cui, “Wireless communications with programmable metasurface: New paradigms, opportunities, and challenges on transceiver design,” IEEE Wireless Commun., vol. 27, no. 2, pp. 180–187, Apr. 2020.
  4. A. Faisal, H. Sarieddeen, H. Dahrouj, T. Y. Al-Naffouri, and M.-S. Alouini, “Ultramassive MIMO systems at terahertz bands: Prospects and challenges,” IEEE Veh. Technol. Mag., vol. 15, no. 4, pp. 33–42, Dec. 2020.
  5. W. Yu, Y. Shen, H. He, X. Yu, S. Song, J. Zhang, and K. B. Letaief, “An adaptive and robust deep learning framework for THz ultra-massive MIMO channel estimation,” IEEE J. Sel. Topics Signal Process., pp. 1–16, to appear, 2023.
  6. H. Zhang, N. Shlezinger, F. Guidi, D. Dardari, M. F. Imani, and Y. C. Eldar, “Beam focusing for near-field multiuser MIMO communications,” IEEE Trans. Wireless Commun., vol. 21, no. 9, pp. 7476–7490, Mar. 2022.
  7. R. Cao, H. He, X. Yu, J. Zhang, S. Song, Y. Gong, and K. B. Letaief, “Belief propagation for near-field cooperative localization and tracking in 6G vehicular networks,” in Proc. IEEE Int. Medit. Conf. Commun. and Netw. (MeditCom), Athens, Greece, Sept. 2022.
  8. M. Cui, Z. Wu, Y. Lu, X. Wei, and L. Dai, “Near-field MIMO communications for 6G: Fundamentals, challenges, potentials, and future directions,” IEEE Commun. Mag., vol. 61, no. 1, pp. 40–46, Jan. 2023.
  9. Y. Shi, L. Lian, Y. Shi, Z. Wang, Y. Zhou, L. Fu, L. Bai, J. Zhang, and W. Zhang, “Machine learning for large-scale optimization in 6G wireless networks,” IEEE Commun. Surv. Tut., to appear, 2023.
  10. W. Liu, H. Ren, C. Pan, and J. Wang, “Deep learning based beam training for extremely large-scale massive MIMO in near-field domain,” IEEE Commun. Lett., vol. 27, no. 1, pp. 170–174, Jan. 2023.
  11. A. Kosasih, V. Onasis, V. Miloslavskaya, W. Hardjawana, V. Andrean, and B. Vucetic, “Graph neural network aided MU-MIMO detectors,” IEEE J. Sel. Areas Commun., vol. 40, no. 9, pp. 2540–2555, Sept. 2022.
  12. Y. Ma, Y. Shen, X. Yu, J. Zhang, S. H. Song, and K. B. Letaief, “Learn to communicate with neural calibration: Scalability and generalization,” IEEE Trans. Wireless Commun., vol. 21, no. 11, pp. 9947–9961, Nov. 2022.
  13. A. M. Elbir, K. V. Mishra, and S. Chatzinotas, “Terahertz-band joint ultra-massive MIMO radar-communications: Model-based and model-free hybrid beamforming,” IEEE J. Sel. Topics Signal Process., vol. 15, no. 6, pp. 1468–1483, Nov. 2021.
  14. Y. Shen, J. Zhang, S. H. Song, and K. B. Letaief, “Graph neural networks for wireless communications: From theory to practice,” IEEE Trans. Wireless Commun., vol. 22, no. 5, pp. 3554–3569, May 2023.
  15. H. He, S. Jin, C.-K. 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, May 2019.
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