RSSI-based Methods for LOS/NLOS Channel Identification in Indoor Scenarios

Carpi, Fabrizio; Davoli, Luca; Martalò, Marco; Cilfone, Antonio; Yu, Yang; Wang, Yi; Ferrari, Gianluigi

doi:10.1109/iswcs.2019.8877315

Cited by 20 publications

(29 citation statements)

References 9 publications

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“…There are several approaches used in scientific research for the detection and separation of the LOS and NLOS propagation paths, which can be mainly categorized into the two classes as mentioned below: Model-driven category, where a statistical model followed by thresholding can be used to separate between LOS and NLOS scenarios, e.g., [ 7 , 8 , 9 , 10 , 11 ]. Data-driven category, where some form of training data for LOS and NLOS cases is available to train some models via feature extraction, followed by some ML classifier stage, e.g., [ 9 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. …”

Section: State-of-the-art In Los Detectionmentioning

confidence: 99%

“…Model-driven category, where a statistical model followed by thresholding can be used to separate between LOS and NLOS scenarios, e.g., [ 7 , 8 , 9 , 10 , 11 ].…”

Section: State-of-the-art In Los Detectionmentioning

confidence: 99%

“…Data-driven category, where some form of training data for LOS and NLOS cases is available to train some models via feature extraction, followed by some ML classifier stage, e.g., [ 9 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: State-of-the-art In Los Detectionmentioning

confidence: 99%

“…Both model-driven and data-driven approaches were studied in [ 9 ] for WiFi signals and Received Signal Strength (RSS) measurements; threshold-based statistical models were compared with NN models for LOS detection; the NN features included standard deviation of the RSS, as well as kurtosis, skewness, hyper-skewness, and Rician factors. NN showed slightly better LOS detection accuracies than threshold-based algorithms reaching 90% classification accuracy or higher.…”

Section: State-of-the-art In Los Detectionmentioning

confidence: 99%

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Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

Jayawardana

Obaid

Yesilyurt

et al. 2023

Sensors

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The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.

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Section: State-of-the-art In Los Detectionmentioning

confidence: 99%

“…Model-driven category, where a statistical model followed by thresholding can be used to separate between LOS and NLOS scenarios, e.g., [ 7 , 8 , 9 , 10 , 11 ].…”

Section: State-of-the-art In Los Detectionmentioning

confidence: 99%

Section: State-of-the-art In Los Detectionmentioning

confidence: 99%

Section: State-of-the-art In Los Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

Jayawardana

Obaid

Yesilyurt

et al. 2023

Sensors

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“…This space regionalization method applies to both Wi-Fi and BLE signals. Specifically, we adopt the classifier in [39] to separate a dataset into smaller ones and analyze their statistical features. In the following section, we present specifically BLE signal classification.…”

Section: ) Small Region Classificationmentioning

confidence: 99%

Smartphone-Based Indoor Tracking in Multiple-Floor Scenarios

Nguyen

Kim³

et al. 2021

IEEE Access

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With the rapid development of location-based services (LBSs), efficient and mobile-friendly localization algorithms should be designed for users to deliver a reliable LBS. In this paper, we present an algorithm with the corresponding smartphone app that enables users to calculate their locations based on representative infrastructures, such as nearby Wi-Fi access points and Bluetooth low energy (BLE) beacons subject to low-cost, rapid system deployment, and competitive location accuracy. Working under indoor multiple-floor scenarios, our app has three prominent features for estimating user locations. First, we establish a feature identifier to detect the current floor and the feasible area in which the user may walk. Second, owing to the structures of the indoor environment and the presence of different obstacles, the unpredictable variation of the received signal strength (RSS) in indoor environments is considered in the RSS-distance relationship to provide accurate location estimates. Third, with the prevalence of smartphones, we extract smartphone-inertial measurement units to learn users' behavior preferences, while collecting reference signals (e.g., Wi-Fi/BLE readings) along the pathway and input to the tracking algorithm. Then, the user's current location is displayed on the app. With this solution, we can provide an accurate location estimate with relatively low computational complexity regarding mobile device capability, while reducing labor costs from traditional fingerprint deployments. Finally, we test our tracking app in real-time multiple floor scenarios and evaluate the collected tracking data. Experimental results show that our proposed scheme achieves an average localization accuracy of more than 80% within a 2-m error bound in multiple-floor scenarios, while all areas (i.e., corridors, rooms, and stairs) were successfully identified.

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