Channel Estimation for 6G Near-Field Wireless Communications: A Comprehensive Survey
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917363849363456 |
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| author | Long, Wen-Xuan Ye, Shengyu Moretti, Marco Morelli, Michele Sanguinetti, Luca Chen, Rui Wang, Cheng-Xiang |
| author_facet | Long, Wen-Xuan Ye, Shengyu Moretti, Marco Morelli, Michele Sanguinetti, Luca Chen, Rui Wang, Cheng-Xiang |
| contents | The sixth-generation (6G) wireless systems are expected to adopt extremely large aperture arrays (ELAAs), novel antenna architectures, and operate in extremely high-frequency bands to meet growing data demands. ELAAs significantly increase the number of antennas, enabling finer spatial resolution and improved beamforming. At high frequencies, ELAAs shift communication from the conventional far-field to near-field regime, where spherical wavefronts dominate and the channel response depends on both angle and distance, increasing channel dimensionality. Conventional far-field channel estimation methods, which rely on angular information, struggle in near-field scenarios due to increased pilot overhead and computational complexity. This paper presents a comprehensive survey of recent advances in near-field channel estimation. It first defines the near- and far-field boundary from an electromagnetic perspective and discusses key propagation differences, alongside a brief review of ELAA developments. Then, it introduces mainstream near-field channel models and compares them with far-field models. Major estimation techniques are reviewed under different configurations (single/multi-user, single/multi-carrier), including both direct estimation and RIS-assisted cascaded estimation. These techniques reveal trade-offs among estimation accuracy, complexity, and overhead. This survey aims to provide insights and foundations for efficient and scalable near-field channel estimation in 6G systems, while identifying key challenges and future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Channel Estimation for 6G Near-Field Wireless Communications: A Comprehensive Survey Long, Wen-Xuan Ye, Shengyu Moretti, Marco Morelli, Michele Sanguinetti, Luca Chen, Rui Wang, Cheng-Xiang Signal Processing Information Theory The sixth-generation (6G) wireless systems are expected to adopt extremely large aperture arrays (ELAAs), novel antenna architectures, and operate in extremely high-frequency bands to meet growing data demands. ELAAs significantly increase the number of antennas, enabling finer spatial resolution and improved beamforming. At high frequencies, ELAAs shift communication from the conventional far-field to near-field regime, where spherical wavefronts dominate and the channel response depends on both angle and distance, increasing channel dimensionality. Conventional far-field channel estimation methods, which rely on angular information, struggle in near-field scenarios due to increased pilot overhead and computational complexity. This paper presents a comprehensive survey of recent advances in near-field channel estimation. It first defines the near- and far-field boundary from an electromagnetic perspective and discusses key propagation differences, alongside a brief review of ELAA developments. Then, it introduces mainstream near-field channel models and compares them with far-field models. Major estimation techniques are reviewed under different configurations (single/multi-user, single/multi-carrier), including both direct estimation and RIS-assisted cascaded estimation. These techniques reveal trade-offs among estimation accuracy, complexity, and overhead. This survey aims to provide insights and foundations for efficient and scalable near-field channel estimation in 6G systems, while identifying key challenges and future research directions. |
| title | Channel Estimation for 6G Near-Field Wireless Communications: A Comprehensive Survey |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2507.23526 |