5G毫米波网络中的用户位置——机器学习射频模式匹配

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI:10.1109/ROPEC55836.2022.10018626

Játiva E. René, A. Salazar, Katty Beltrán, Oliver Caisaluisa

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引用次数: 1

摘要

使用Open Street Maps和Matlab®在基多旧金山大学(USFQ)校园内部署了一个以28 GHz毫米波工作的逼真模拟5G DM-MIMO无线网络。在基站天线阵列采集接收到的信号强度指纹，利用k近邻法对接收到的射频模式与存储的指纹进行匹配。我们测试了三种不同的方法并比较了它们的结果，所有的病例都显示出非常好的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching

A realistic simulated 5G DM-MIMO wireless network operating at 28 GHz mmWaves has been deployed using Open Street Maps and Matlab® over the campus of Universidad San Francisco de Quito (USFQ). Received Signal Strength fingerprints have been collected at Base Station antenna array, and the K-Nearest Neighbors method has been used to perform the match between the received RF patterns and the stored fingerprints. Three different procedures were tested and their results were compared, exhibiting very good outcomes in all the cases.

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来源期刊

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

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