Abstract
Channel knowledge map (CKM) is a promising technology that can build a bridge between the environments and communication strategies for joint sensing and communication. This paper proposes a CKM construction framework involving multi-dimensional channel information in three-dimensional (3D) space including the channel knowledge extraction and completion of sparse channel knowledge. For channel knowledge extraction, the constant false alarm probability (CFAR) method is utilized to extract the effective channel knowledge from the channel impulse response (CIR). On this basis, the Kriging interpolation method is used to complete the environment-dependent channel knowledge. Finally, the proposed CKM construction method is verified based on the simulations. Simulation results show that the sparse channel knowledge is well extracted and completed. The constructed CKMs are consistent with the reference CKMs.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (No. 62271250), in part by Natural Science Foundation of Jiangsu Province (No. BK20211182), in part by the Key Technologies R & D Program of Jiangsu (Prospective and Key Technologies for Industry) under Grants BE2022067 and BE2022067-3.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Qiu, Y. et al. (2024). Channel Knowledge Extraction and Completion Methods for 3D CKM Construction. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_30
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DOI: https://doi.org/10.1007/978-981-99-7502-0_30
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