Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques
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arXiv
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| Format: | Preprint |
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2023
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| author | Yu, Wentao Ma, Yifan He, Hengtao Song, Shenghui Zhang, Jun Letaief, Khaled B. |
| author_facet | Yu, Wentao Ma, Yifan He, Hengtao Song, Shenghui Zhang, Jun Letaief, Khaled B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_09575 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques Yu, Wentao Ma, Yifan He, Hengtao Song, Shenghui Zhang, Jun Letaief, Khaled B. Signal Processing Information Theory 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. |
| title | Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2309.09575 |