Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909199101853696 |
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| author | Xu, Bokai Zhang, Jiayi Du, Hongyang Wang, Zhe Liu, Yuanwei Niyato, Dusit Ai, Bo Letaief, Khaled B. |
| author_facet | Xu, Bokai Zhang, Jiayi Du, Hongyang Wang, Zhe Liu, Yuanwei Niyato, Dusit Ai, Bo Letaief, Khaled B. |
| contents | Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless systems. The larger array aperture of XL-MIMO makes communication scenarios closer to the near-field region. Therefore, near-field resource allocation is essential in realizing the above key performance indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily depends on the channel characteristics of the selected users, eliminating interference between users through beamforming, power control, etc. The above resource allocation issue constitutes a complex joint multi-objective optimization problem since many variables and parameters must be optimized, including the spatial degree of freedom, rate, power allocation, and transmission technique. In this article, we review the basic properties of near-field communications and focus on the corresponding "resource allocation" problems. First, we identify available resources in near-field communication systems and highlight their distinctions from far-field communications. Then, we summarize optimization tools, such as numerical techniques and machine learning methods, for addressing near-field resource allocation, emphasizing their strengths and limitations. Finally, several important research directions of near-field communications are pointed out for further investigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17868 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks Xu, Bokai Zhang, Jiayi Du, Hongyang Wang, Zhe Liu, Yuanwei Niyato, Dusit Ai, Bo Letaief, Khaled B. Information Theory Signal Processing Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless systems. The larger array aperture of XL-MIMO makes communication scenarios closer to the near-field region. Therefore, near-field resource allocation is essential in realizing the above key performance indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily depends on the channel characteristics of the selected users, eliminating interference between users through beamforming, power control, etc. The above resource allocation issue constitutes a complex joint multi-objective optimization problem since many variables and parameters must be optimized, including the spatial degree of freedom, rate, power allocation, and transmission technique. In this article, we review the basic properties of near-field communications and focus on the corresponding "resource allocation" problems. First, we identify available resources in near-field communication systems and highlight their distinctions from far-field communications. Then, we summarize optimization tools, such as numerical techniques and machine learning methods, for addressing near-field resource allocation, emphasizing their strengths and limitations. Finally, several important research directions of near-field communications are pointed out for further investigation. |
| title | Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2310.17868 |