Okumura-Hata Model for LTE
The Hata Model for Urban Areas, also known as the Okumura-Hata model for being a developed version of the Okumura Model, is the most widely used radio frequency propagation model for predicting the behavior of cellular propagation in built up areas. This model incorporates the graphical information from Okumura model and develops it further to realize the effects of diffraction, reflection and scattering caused by city structures.
Okumura model was originally built into three modes, one for urban, suburban and open areas. The model for urban areas was built first and used as the base for others The Okumura Hata model also has two more varieties for propagation in Suburban Areas and Open Areas. The original Okumura model for Urban Areas is a radio propagation model that was built using the data collected in the city of Tokyo, Japan.
The model is ideal for using in cities with many urban structures but not many tall blocking structures. The model served as a base for the Hata Model and the following assumptions apply to the use of Okumura Hata model.
Frequency: 150 MHz to 1500 MHz in Okumura-Hata Model
Mobile Station Antenna Height: between 1 m and 10 m in Okumura-Hata Model
Base station Antenna Height: between 30 m and 200 m in Okumura-Hata Model
Link distance: between 1 km and 20 km in Okumura-Hata Model
The Okumura-Hata model for LTE is a propagation model that estimates signal coverage in a Long-Term Evolution (LTE) network based on factors such as frequency, distance, and environment. It’s used to predict signal strength and coverage in urban, suburban, and rural areas, aiding in network planning and optimization.
Okumura-Hata Model for LTE
The Okumura-Hata Model is a popular propagation model you can use for LTE network planning, especially in urban, suburban, and rural areas. It helps estimate path loss based on factors like frequency, distance, and terrain. I’d recommend using this model when you’re working in environments similar to the ones it’s designed for, as it accounts for variations in building density and landscape. It’s a good choice if you need a more accurate prediction of signal behavior over medium to long distances.