TY - GEN
T1 - Ground Level Mobile Signal Prediction Using Higher Altitude UAV Measurements and ANN
AU - Saadi, Ibtihal Al
AU - Tarhuni, Naser
AU - Mesbah, Mostefa
N1 - Funding Information:
This research was supported by Omantel grant no. (EG/SQU-OT/21/01), Sultanate of Oman, in frame of the research project: “Mobile Network Coverage Assessment using Unmanned Aerial Vehicle and Artificial Intelligence”. The authors would like to thank Embedded & Interconnected Vision Systems Laboratory at Sultan Qaboos University for helping in the drone measurements.
Publisher Copyright:
© 2022 FRUCT Oy.
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2022/11/9
Y1 - 2022/11/9
N2 - Testing the mobile network signal strength is essential for evaluating actual user experience. This procedure is done by measurement campaign, where a person or a group of people walk or drive through the target area holding a measuring equipment. However, this is not suitable to do in hard-to-reach areas. In order to minimize human involvement and to reduce resources, labour, and time consumed, an alternative approach for physical assessment of cellular coverage and quality evaluating is needed. In this work, we used a drone to measure mobile network signal strength to generate a two-dimensional coverage map for difficult-to-reach areas. A machine learning algorithm is used to estimate the signal strength in other locations within the area to generate a dense 2D coverage map. The measurements were done on Sultan Qaboos University Campus, Muscat, Oman. Our finding shows that a drone equipped with a low-cost signal strength measuring device and an artificial neural network (ANN) algorithm are able to generate an accurate dense map of mobile signal strength in a flexible and cost-effective manner. The ANN was capable of predicting the signal strength at the ground from measurement at higher altitudes with an accuracy of 97%.
AB - Testing the mobile network signal strength is essential for evaluating actual user experience. This procedure is done by measurement campaign, where a person or a group of people walk or drive through the target area holding a measuring equipment. However, this is not suitable to do in hard-to-reach areas. In order to minimize human involvement and to reduce resources, labour, and time consumed, an alternative approach for physical assessment of cellular coverage and quality evaluating is needed. In this work, we used a drone to measure mobile network signal strength to generate a two-dimensional coverage map for difficult-to-reach areas. A machine learning algorithm is used to estimate the signal strength in other locations within the area to generate a dense 2D coverage map. The measurements were done on Sultan Qaboos University Campus, Muscat, Oman. Our finding shows that a drone equipped with a low-cost signal strength measuring device and an artificial neural network (ANN) algorithm are able to generate an accurate dense map of mobile signal strength in a flexible and cost-effective manner. The ANN was capable of predicting the signal strength at the ground from measurement at higher altitudes with an accuracy of 97%.
UR - http://www.scopus.com/inward/record.url?scp=85143902909&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143902909&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/77dde228-2670-368a-b8b6-fdfd837c52f9/
U2 - 10.23919/fruct56874.2022.9953813
DO - 10.23919/fruct56874.2022.9953813
M3 - Conference contribution
AN - SCOPUS:85143902909
SN - 9789526924489
T3 - Conference of Open Innovation Association, FRUCT
SP - 15
EP - 21
BT - Proceedings of the 32nd Conference of Open Innovations Association FRUCT, FRUCT 2022
PB - IEEE Computer Society
T2 - 32nd Conference of Open Innovations Association FRUCT, FRUCT 2022
Y2 - 9 November 2022 through 11 November 2022
ER -