Neural-Network-based NLOS Identification in Angular Domain at 60-GHz
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866917596180250624 |
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| author | Lyu, Pengfei Benlarbi-Delaï, Aziz Ren, Zhuoxiang Sarrazin, Julien |
| author_facet | Lyu, Pengfei Benlarbi-Delaï, Aziz Ren, Zhuoxiang Sarrazin, Julien |
| contents | This paper introduces an identification method that determines whether a millimeter-wave wireless transmission using directional antennas is being established over a line-of-sight (LOS) or a non-line-of-sight (NLOS) cluster for indoor localization applications. The proposed technique utilizes the channel power angular spectrum that is readily available from a beam training process. In particular, the behavior of five different channel metrics, namely the spatial-domain, time-domain, and frequency-domain channel kurtosis, the mean excess delay, and the RMS delay spread, is analyzed using maximum likelihood ratio and artificial neural network. A noticeable difference between LOS and NLOS clusters is observed and assessed for identification. Hypothesis testing shows errors as low as 0.01-0.02 in simulation and 0.04-0.07 in measurements at 60 GHz. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_09343 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Neural-Network-based NLOS Identification in Angular Domain at 60-GHz Lyu, Pengfei Benlarbi-Delaï, Aziz Ren, Zhuoxiang Sarrazin, Julien Signal Processing This paper introduces an identification method that determines whether a millimeter-wave wireless transmission using directional antennas is being established over a line-of-sight (LOS) or a non-line-of-sight (NLOS) cluster for indoor localization applications. The proposed technique utilizes the channel power angular spectrum that is readily available from a beam training process. In particular, the behavior of five different channel metrics, namely the spatial-domain, time-domain, and frequency-domain channel kurtosis, the mean excess delay, and the RMS delay spread, is analyzed using maximum likelihood ratio and artificial neural network. A noticeable difference between LOS and NLOS clusters is observed and assessed for identification. Hypothesis testing shows errors as low as 0.01-0.02 in simulation and 0.04-0.07 in measurements at 60 GHz. |
| title | Neural-Network-based NLOS Identification in Angular Domain at 60-GHz |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2107.09343 |