A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks

IEEE Access

Ricardo Santos¹  Thaina Saraiva¹  Pedro Vieira²  Dinis Moreira²  Pedro Alves²  David Duarte³  Hugo Gamboa³  Ricardo Leonardo⁴  Marilia Barandas⁴ 

[1] &x00E3;Associa&x00E7;Consultoria em Telecomunicações, Celfinet, Cruz Quebrada, Portugal;o Fraunhofer Portugal Research, Porto, Portugal;

关键词: Indoor outdoor detection;    machine learning algorithms;    long term evolution;    measurement campaigns;    smartphone;    network traces;   

DOI  :  10.1109/ACCESS.2021.3130429

来源: DOAJ

【 摘 要 】

The ability to locate users and estimate traffic in mobile networks is still one of the major challenges when it comes to planning and optimizing the networks. Since indoor location is not always possible or precise, having the ability to distinguish indoor from outdoor traffic can be a valuable alternative and/or improvement. In this paper, two different machine learning algorithms are presented to classify a user’s environment, whether indoor or outdoor, using only data from a Long Term Evolution (LTE) network. To test both algorithms, two different measurement campaigns were done. Both campaigns used a smartphone to gather data from the user’s side. The first measurement campaign was done across 6 different cities, ranging from small rural areas to large urban environments, while the second was only done on a large urban city. On the second campaign, Network Traces (NT) data was also collected from the network side. The first algorithm consists on a Random Forest (RF) and the second relies on a Long Short Term Memory (LSTM), thus covering both more traditional machine learning and deep learning approaches. The results varied from 0.75 to 0.91 on the F1-Score, depending on the validation strategy, showing promising results.

【 授权许可】

Unknown