A Tutorial on Near-Field XL-MIMO Communications Towards 6G
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Abstract (arXiv)
Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for the sixth-generation (6G) mobile communication networks. By significantly boosting the antenna number or size to at least an order of magnitude beyond current massive MIMO systems, XL-MIMO is expected to unprecedentedly enhance the spectral efficiency and spatial resolution for wireless communication. The evolution from massive MIMO to XL-MIMO is not simply an increase in the array size, but faces new design challenges, in terms of near-field channel modelling, performance analysis, channel estimation, and practical implementation. In this article, we give a comprehensive tutorial overview on near-field XL-MIMO communications, aiming to provide useful guidance for tackling the above challenges. First, the basic near-field modelling for XL-MIMO is established, by considering the new characteristics of non-uniform spherical wave (NUSW) and spatial non-stationarity. Next, based on the near-field modelling, the performance analysis of XL-MIMO is presented, including the near-field signal-to-noise ratio (SNR) scaling laws, beam focusing pattern, achievable rate, and degrees-of-freedom (DoF). Furthermore, various XL-MIMO design issues such as near-field beam codebook, beam training, channel estimation, and delay alignment modulation (DAM) transmission are elaborated. Finally, we point out promising directions to inspire future research on near-field XL-MIMO communications.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2023.
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Citing papers per year: 2024: 2; 2025: 3; 2026: 2.
7 papers.
- 2024Generative CKM Construction using Partially Observed Data with Diffusion Modelcited by 4
- 2026U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approachcited by 4
- 2024Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Imagescited by 1
- 2025Near Field Localization via AI-Aided Subspace Methodscited by 1
- 2025Cross-field SNR Analysis and Tensor Channel Estimation for Multi-UAV Near-field Communicationscited by 0
- 2025LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimationcited by 0
- 2026Hybrid Mamba-Attention Neural Architecture for Channel Estimationcited by 0
- 2026U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach6 Mar 2026
- 2026Hybrid Mamba-Attention Neural Architecture for Channel Estimation23 Jan 2026
- 2025LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimation28 Nov 2025
- 2025Cross-field SNR Analysis and Tensor Channel Estimation for Multi-UAV Near-field Communications23 Aug 2025
- 2025Near Field Localization via AI-Aided Subspace Methods1 Apr 2025
- 2024Generative CKM Construction using Partially Observed Data with Diffusion Model19 Dec 2024
- 2024Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images11 Jan 2024