Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

Author:

Del Corte-Valiente AntonioORCID,Gómez-Pulido José Manuel,Gutiérrez-Blanco OscarORCID,Castillo-Sequera José LuisORCID

Abstract

The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)

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1. IODnet: Indoor/Outdoor Telecommunication Signal Detection through Deep Neural Network;2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC);2023-12-18

2. Real-Time Outdoor Localization Using Radio Maps: A Deep Learning Approach;IEEE Transactions on Wireless Communications;2023-12

3. Locswinunet: A Neural Network for Urban Wireless Localization Using TOA and RSS Radio Maps;2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP);2023-09-17

4. A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN;Future Internet;2022-07-29

5. LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning;ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2022-05-23

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