LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:

OpenAIRE is about to release its new face with lots of new content and services.
During September, you may notice downtime in services, while some functionalities (e.g. user registration, login, validation, claiming) will be temporarily disabled.
We apologize for the inconvenience, please stay tuned!
For further information please contact helpdesk[at]openaire.eu

fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Udofia, Kufre M.; Friday, Nwiido; Jimoh, Afolayan J. (2016)
Publisher: Mathematical and Software Engineering
Journal: Mathematical and Software Engineering
Languages: English
Types: Article
Subjects: Pathloss; Residual; Pathloss Model; Okumura-Hata Model; Model Tuning; RMSE Based Tuning; Composite Function; Composite Function of Residual
In this paper, an innovative composite function of prediction residual-based approach for tuning Okumura-Hata propagation model in the 800-900MHz GSM frequency band is presented. The study is based on empirical measurements conducted at University Of Uyo (UNIUYO) town-campus located at latitude and longitude of 5.042976, 7.919046 respectively. The proposed path loss tuning approach is compared with RMSE based tuning approach. According to the results, the composite function of prediction residual tuned Okumura-Hata model has the lowest RMSE value of 2.164, the highest Coefficient Of Determination (R^2) value of  0.967 and the highest prediction accuracy of  98.64%. On the other hand , the RMSE- tuned Okumura-Hata model has a higher  RMSE value of 5.3, lower R^2 value of 0.814 and the lower prediction accuracy of 96.87%. Essentially, in all the three performance measures used , the composite function of prediction residual based tuning approach performed better than the RMSE based tuning approach. However, in pathloss tuning studies, RMSE value below  7dB is acceptable for the urban area. As such, the RMSE based tuning approach gave tuned model with acceptable RMSE value but with lower prediction accuracy than the model produced by the composite function of prediction residual based tuning approach.

Share - Bookmark

Cite this article

Cookies make it easier for us to provide you with our services. With the usage of our services you permit us to use cookies.
More information Ok