Hata Model Path Loss Optimization using Least Mean Square Regression

Authors

Deepti Kakkar
Department of Electronics and Communication Engineering, Dr. BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India
Aditi Bharmaik
Department of Electronics and Communication Engineering, Dr. BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India
Ankita Sharma
Department of Electronics and Communication Engineering, Dr. BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India
Eshwari S.S. Dagar
Department of Electronics and Communication Engineering, Dr. BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India
Parul Rattanpal
Department of Electronics and Communication Engineering, Dr. BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India
Shefali Sharma
Department of Electronics and Communication Engineering, Dr. BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India

Synopsis

Astochastic approach based optimization technique to optimize the Hata model path loss equation is presented in this paper. In this paper, the existing Hata model equation for determining path loss in medium urban city is optimized using Least Mean Square regression method. Out of various path loss models available, Hata model was chosen due to its accuracy and reliability in an urban propagation environment. The optimization technique proposed is applied to get the optimumcoefficients of Hata propagation model equation. This stochastic approach is based on reducing the mean square difference between the measured and predicted path loss by adjusting the error coefficients of MSE through regression. The MSE obtained after optimization is significantly lower than that obtained from the existing Hata model. For better planning and implementation of mobile cellular networks there is a need for modifying the existing path loss prediction models. This optimized model can be used to improve the quality of service in 900MHz band in a medium sized urban environment.

WREC21
Published
September 22, 2021
Online ISSN
2582-3922