室内环境下LOS和NLOS识别的深度学习方法

2020 Baltic URSI Symposium (URSI) Pub Date : 2020-10-05 DOI:10.23919/URSI48707.2020.9253757

Alicja Olejniczak, Olga Blaszkiewicz, K. Cwalina, Piotr Rajchowski, J. Sadowski

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引用次数: 3

摘要

由于空间有限及墙壁、家具等障碍，室内环境可能对室内无线电通讯服务的操作造成恶劣和干扰。本文提出了FNN(前馈神经网络)方法用于LOS(视距)和NLOS(非视距)识别，这可能有助于减轻这种负面影响。根据使用UWB(超宽带)无线电模块收集的真实室内测量数据，对所描述的FNN架构进行了评估。

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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Deep Learning Approach for LOS and NLOS Identification in the Indoor Environment

Due to confined spaces and various obstacles e.g. walls, furniture, indoor environment may be considered as a harsh and disturbing in terms of the indoor radiocommunication services operation. The given paper presents FNN (Feedforward Neural Network) method for LOS (Line-Of-Sight) and NLOS (Non-Line-Of-Sight) identification which may support mitigation of such a negative influence. Described FNN architecture was evaluated based on a real indoor measurements collected with the use of the UWB (Ultra Wideband) radio modules.

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2020 Baltic URSI Symposium (URSI)

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