Simple RSRP Fingerprint Collection Setup and Indoor Positioning in 5G
(2022) EITM01 20212Department of Electrical and Information Technology
- Abstract (Swedish)
- Solving for a way to accurately predict the position of a user equipment is
crucial in an array of applications within 5G new radio. One approach is to
form unique identifiers, also known as fingerprints, and map them to positional
data in an area. Previous research suggests that radio signal received strength
is a promising indicator to use for the fingerprint, in order to achieve accurate
indoor positioning.
The thesis proposes an automated data collection setup. It is capable of col-
lecting radio signal information through modem logs. Additionally, it uses a
navigation system which offers a way to map modem log data to a ground-truth
coordinate. A data preprocessing pipeline is presented as to turn the raw modem
logs into... (More) - Solving for a way to accurately predict the position of a user equipment is
crucial in an array of applications within 5G new radio. One approach is to
form unique identifiers, also known as fingerprints, and map them to positional
data in an area. Previous research suggests that radio signal received strength
is a promising indicator to use for the fingerprint, in order to achieve accurate
indoor positioning.
The thesis proposes an automated data collection setup. It is capable of col-
lecting radio signal information through modem logs. Additionally, it uses a
navigation system which offers a way to map modem log data to a ground-truth
coordinate. A data preprocessing pipeline is presented as to turn the raw modem
logs into fingerprints.
The outcome is a data set for supervised learning. As a proof-of-concept
the data is classified using two machine learning algorithms: naive bayes and k
nearest neighbor. In the latter, a F1 (macro) score of 98% is obtained in predicting
the user equipment position. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9092127
- author
- Flink, Sofi LU and Tallund, Annie LU
- supervisor
- organization
- course
- EITM01 20212
- year
- 2022
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- 5G, indoor positioning, radio fingerprint, k nearest neighbor, naive bayes, automated data collection
- report number
- LU/LTH-EIT 2022-880
- language
- English
- id
- 9092127
- date added to LUP
- 2022-06-22 10:57:37
- date last changed
- 2022-06-22 10:57:37
@misc{9092127, abstract = {{Solving for a way to accurately predict the position of a user equipment is crucial in an array of applications within 5G new radio. One approach is to form unique identifiers, also known as fingerprints, and map them to positional data in an area. Previous research suggests that radio signal received strength is a promising indicator to use for the fingerprint, in order to achieve accurate indoor positioning. The thesis proposes an automated data collection setup. It is capable of col- lecting radio signal information through modem logs. Additionally, it uses a navigation system which offers a way to map modem log data to a ground-truth coordinate. A data preprocessing pipeline is presented as to turn the raw modem logs into fingerprints. The outcome is a data set for supervised learning. As a proof-of-concept the data is classified using two machine learning algorithms: naive bayes and k nearest neighbor. In the latter, a F1 (macro) score of 98% is obtained in predicting the user equipment position.}}, author = {{Flink, Sofi and Tallund, Annie}}, language = {{eng}}, note = {{Student Paper}}, title = {{Simple RSRP Fingerprint Collection Setup and Indoor Positioning in 5G}}, year = {{2022}}, }