arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2102.04892cs.ITcs.LGeess.SPmath.IT
收藏

Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data

Ericsson AB(爱立信公司)

B. R. Manoj, Guoda Tian, Sara Gunnarsson, Fredrik Tufvesson, Erik G. Larsson

英文摘要:

Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑