TY - GEN
T1 - Channel Covariance based Fingerprint Localization
AU - Li, Xinze
AU - Al-Tous, Hanan
AU - Hajri, Salah Eddine
AU - Tirkkonen, Olav
PY - 2024
Y1 - 2024
N2 - We study performance and complexity of fingerprint localization based on 5G signaling. We concentrate on channel covariance and Channel Impulse Response (CIR) features, studying the effect of several factors on the localization performance such as the channel bandwidth, the number of Base Stations (BSs), the number of antennas at each BS, and the number of time samples. We consider Weighted K Nearest Neighbour (WKNN) as well as Deep Neural Network (DNN) localization. We adopt DNNs based on the Rel-18 3GPP Study Item AI/ML for positioning accuracy enhancement. Simulation results show that channel covariance features outperform CIR in terms of localization accuracy. Furthermore, covariance-based features are robust with respect to bandwidth reduction, allowing for more power-efficient implementations. However, a noticeable dependency on the number of BSs, BS antennas, and time samples, is found. Results also show that increasing sampling density is much more beneficial for improving performance with CIR-based features. Again this highlights the power saving virtues of using covariance based features as input. Finally, results show that WKNN performs better with covariance-based features, with noticeable degradation in performance, when CIR features are used instead
AB - We study performance and complexity of fingerprint localization based on 5G signaling. We concentrate on channel covariance and Channel Impulse Response (CIR) features, studying the effect of several factors on the localization performance such as the channel bandwidth, the number of Base Stations (BSs), the number of antennas at each BS, and the number of time samples. We consider Weighted K Nearest Neighbour (WKNN) as well as Deep Neural Network (DNN) localization. We adopt DNNs based on the Rel-18 3GPP Study Item AI/ML for positioning accuracy enhancement. Simulation results show that channel covariance features outperform CIR in terms of localization accuracy. Furthermore, covariance-based features are robust with respect to bandwidth reduction, allowing for more power-efficient implementations. However, a noticeable dependency on the number of BSs, BS antennas, and time samples, is found. Results also show that increasing sampling density is much more beneficial for improving performance with CIR-based features. Again this highlights the power saving virtues of using covariance based features as input. Finally, results show that WKNN performs better with covariance-based features, with noticeable degradation in performance, when CIR features are used instead
KW - weighted K nearest neighbours
KW - fingerprint localization
KW - channel covariance
KW - Channel state information
KW - deep neural networks
UR - http://www.scopus.com/inward/record.url?scp=85213039654&partnerID=8YFLogxK
U2 - 10.1109/VTC2024-Fall63153.2024.10757910
DO - 10.1109/VTC2024-Fall63153.2024.10757910
M3 - Conference article in proceedings
T3 - IEEE Vehicular Technology Conference
BT - 2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings
PB - IEEE
T2 - IEEE Vehicular Technology Conference
Y2 - 7 October 2024 through 10 October 2024
ER -