The DOI matches its Crossref record; 129 of the 129 references it cites were found in a registry or an index.
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
Open accessAbstract
Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, e.g., the estimated channels or beams between base station (BS) and user equipment (UE), making it possible to better learn the local wireless environment. By exploiting this new opportunity and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, followed by the comparison of CKM with various existing channel inference techniques. Next, the main techniques for CKM construction are discussed, including both environment model-free and environment model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work.
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Self-citationSpatially Sparse Precoding in Millimeter Wave MIMO Systems
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Metadata
- Authors
- Zeng, Y., Chen, J., Xu, J., Wu, D., Xu, X., Jin, S., Gao, X., Gesbert, D., Cui, S., & Zhang, R.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2024
- Volume
- 26
- Issue
- 3
- Pages
- 1478-1519
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W4391697040
- Cited by
- 303
- References
- 129
- FWCI
- 31.26
- Type
- journal article
- Language
- en
- Open access
- green
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