Statistical analysis of atmospheric parameters, noise temperature and digital terrestrial television signal strength over Jos metropolis, Plateau state, Nigeria

Abstract

The study of the relationship between the Received Signal Strengths (RSS) from Unity TV (786 MHz) located at Rayfield (9.84 o North 8.89 o East), Jos, Plateau State and some atmospheric parameters with noise temperature has been done using two years half-hourly data. The measurement was carried from 5 am and 9 pm daily. The analysis was carried out using the regression, co-integration and Granger causality models. The results show that the RSS is higher in the morning hours (5.00 am–9.00 am) late in the evening (6.30 pm–9.00 pm) but lower between 9.00 am and 6.30 pm. This might be due to the “dusty” atmosphere and low reflection of ionosphere in the day time which causes signal attenuation within the day time. The correlation coefficients from the coefficient of determination (R squared) of 0.88, 0.004, 0.838. 0.25, 0.867, and 0.112, are −0.94, −0.07, 0.92, −0.50, −0.93, −0.334 for atmospheric temperature, pressure, relative humidity, wind speed, noise temperature and rainfall respectively. It means all the independent variables except relative humidity are inversely proportional with the RSS. Also, the higher R2 values for atmospheric temperature, relative humidity and noise temperature respectively, indicates higher correlation between RSS and these parameters. It can be inferred that the models are suitable for predicting Unity TV (UTV) signal strength and that of other TV stations in Jos and environs, using T, RH and NT as inputs. The results also showed that there is a co-integration and Granger Causal relationship between the parameters and RSS.The result will find applications to the Nigeria Broadcasting Commission in the assigning of frequencies taking atmospheric parameters into considerations, Radio Engineers and Scientists, Satellite link designers, and mobile communication experts for the link budget design and appropriate equipment deployment based on the atmospheric condition on the geographical areas.