 Open Access
 Authors : Joseph Mom , Soo Tyokighir , Gabriel Igwue
 Paper ID : IJERTV10IS090071
 Volume & Issue : Volume 10, Issue 09 (September 2021)
 Published (First Online): 14092021
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Modification of the ITUR P.53017 Rain Attenuation Prediction Model
Joseph Mom
Department of Electrical and Electronics Engineering Joseph Sarwuan Tarka University,
Makurdi, Nigeria
Soo Tyokighir
Department of Electrical and Electronics Engineering Joseph Sarwuan Tarka University,
Makurdi, Nigeria
Gabriel Igwue
Department of Electrical and Electronics Engineering Joseph Sarwuan Tarka University,
Makurdi, Nigeria
Abstract Rain is the major factor that affects the propagation of signals, especially above 10 GHz frequency. To perform adequate link budget planning, the propagation impairments posed on microwave links by rain needs investigation. The ITU R recommends a global rain attenuation prediction model for terrestrial and earthspace links, but the model is based on propagation measurement missions in temperate region hence not suitable for tropical locations. In this study, a modification to the existing ITUR P. 53017 rain attenuation prediction model is proposed. The modified model is implemented based on measurement data collected from four geographically spread terrestrial microwave DIGI MINILINKs operating at 1518 GHz frequency. The curve fitting technique is used to modify the second constant in the ITUR P.53017 model to 0.743. Results suggest that for rain rates ranging from 20 mm/h to 100 mm/h, the modified ITUR model reduced the root mean square (RMS) error value by at least 5.46%. Therefore the modified ITUR P.53017 model is suitable for rain attenuation prediction in tropical climates and locations characterized by heavy yearly rain.
KeywordsRain attenuation, ITUR P.53017, Tropical climate, Terrestrial link, RMS Error

INTRODUCTION
The propagation of highfrequency waves especially in rain has become an issue for radio engineers over recent years. A microwave signal can be scattered, diffracted absorbed, or depolarized by rain. This can restrict signal propagation hence decrease the quality of service. Above 10 GHz, rain poses deleterious effects to terrestrial and satellite toearth communication links [1], [2], [3], [4]. In the design and planning of reliable communication systems, rain attenuation prediction studies become necessary [5]. The emergence and development of complex radio access networks have increased spectrum occupancy necessitated by the demand for higher bandwidths. It then becomes imperative to employ the advantages of higher frequencies that are capable of supporting these demands [6], [7], [8], [9]. Over the years, some rain attenuation prediction models have been developed by different researchers. Some key parameters used for most of the rain attenuation models include rainfall rate, frequency, specific attenuation, and
elevation angle [10]. Short integration time rainfall rate is also one of the essential input parameters in rain attenuation prediction models. Some prediction models in South Korea have used the polynomial regression fitting method for the estimation of oneminute rainfall rate distribution [11], [12], [13]. The major issue with recent rain attenuation prediction models is their poor performance when used in tropical countries such as Nigeria. Considerable disparities are known to exist between the measured and predicted rain attenuation values. Research has also shown that rain structures in temperate regions differ from the ones in tropical locations. Because of this, the rain attenuation prediction model described in [14] is not sufficiently accurate hence the need for modification.

RESEARCH METHOD

Experimental procedure
Define The rain attenuation data were retrieved from four DiGi telecommunications stations (Abuja, Plateau, Sokoto, Edo) in Nigeria each consisting of microwave MINILINK having data acquisition, retrieval, and processing system. The antennas are horizontally polarized and are strategically located inbetween buildings to remove interference from strong winds. The antenna is frequently sprayed with WD 40 spray to eliminate wet antenna effects. By doing this, interference from other hydrometeor sources becomes negligible. Specifications of the MINILINKS stations are shown in Table 1(a) while the measurement site characteristics are shown in Table 1(b). The signal monitoring device at the receiving stations alongside a computer system is used to monitor the signal strength at one minute intervals for rainy and clear sky conditions. The path attenuation is calculated by finding the difference between the received signal level (RSL) during clear sky conditions and the RSL during rainy conditions for horizontally polarized signals at various rain rates. The equation is given as follows:
Attenuation (dB) = RSL clear sky – RSL rainy (1)
Table 1a. Specifications of the link
Type of antenna
Frontfed
Frequency band
1518 GHz
Polarization
Horizontal
Max transmit power Antenna beamwidth Dynamic range (dB) Antenna size (m) Gain (dBi)
+16.0 dBm
1.9Â°
45
0.9
35
City
Latitude Â°N
Longitude
Â°E
Hs (m)
Path length
Plateau
9.21
9.51
1217
3.26
Abuja
9.07
7.39
360
1.5
Edo Sokoto
6.63
13.00
5.93
5.24
150
265
4.19
1.22
City
Latitude Â°N
Longitude
Â°E
Hs (m)
Path length
Plateau
9.21
9.51
1217
3.26
Abuja
9.07
7.39
360
1.5
Edo Sokoto
6.63
13.00
5.93
5.24
150
265
4.19
1.22
Table 1b. Measurement site characteristics

Weather monitoring station
The rain rate data has been obtained from a locally built rain gauge with a 0.7 mm bucket size. A small 12 watt and 6 volt solar panel are used to ensure continuous data collection and processing. The average of the 10year rain rate data obtained from the Nigerian Meteorological Agency (NiMet) was used for the four sites (Abuja, Plateau, Sokoto, and Edo). The Chebil model [17] was used to convert the 3hr rain data integration time to 1min integration time as recommended by the ITU. Figure 1a shows the map of Nigeria containing regions of interest while Figure 1b shows the graph of attenuation vs. rainfall for the selected stations.

Site details
October.
October.
According to the ITUR rainfall climatic zoning, Nigeria falls in the region 'K' with an average annual rain accumulation exceeding 2000 mm in the south, 3000 mm in the southeastern part and below 1500 mm in most parts of the North. The Nigerian climate is considered tropical and is characterized by wet and dry seasons. The wet season occurs due to northeast trade winds from November to March while the dry season occurs due to southeast trade winds from April to
Fig 1a. Map of Nigeria showing selected stations
Fig 1b. Probability plots: Rain ratevs. rain attenuation

Modified ITUR P.53017 rain attenuation model
The concept of the proposed ITUR model remains the same as the existing one. The modified ITUR model is proposed based on the curve fitting technique with the goal of reducing the prediction error of the existing model. The root mean square error obtained from the existing ITUR model exceeds 10% [15] hence, the need for reduction. The modified ITUR model is based on measured data from a tropical location. The measured values from the MINILINKs are used to obtain the coefficients for the new model. The rain rate for 99.99 % availability is one of the variables utilized by the modified model. The percentage of time for the range 0.001 to 1 is used to determine the coefficient of the newly modified model. The curvefitting function in the Microsoft Excel suite is used to determine the coefficients of the modified model while the solver function was used to solve the nonlinear equation of the ITUR model using the sum of square residuals method to best fit the measured data. Hence, the coefficient that best fits the existing ITUR model is obtained based on measured data. The measured rain attenuation value and percentage of time for a range of 0.001
% to 1 % are used as input data to determine the coefficients. The new coefficients obtained from the curve fitting function are significantly fit with measured data obtained from tropical climate. The second coefficient for the existing ITUR model for prediction of attenuation for other percentages of time p is 0.6082. However, to fit the data in a tropical climate, the coefficient has been replaced with 0.743. By implementing the modified model, the percentage error and RMS error can be significantly reduced. The modified model is shown step by step from equations (2) to (6) as follows:
We calculate the attenuation for p percentage of time as:
(2)
Where:
And:
(3)
Where: k and are obtained using Recommendation ITUR P.8383 and point rainfall rate with one minute integration time (R0.01) is obtained using the method described in [17]. The distance factor r is obtained as follows:
And p is the percentage probability for an average year. While A0.01 is the rain attenuation exceeded at 0.01% given as:
(dB) (4)
Where: R is the specific attenuation given as:
(5)
is the effective pat length given as follows:
(km) (6)
Where:
d and r are the link distance and path reduction factor respectively

Budalal model
The model described in [16] is based on the measured attenuation of smaller than 1 km terrestrial link and frequency of 26/38 GHz. Due to inconsistencies found on the ITUR P.53017 Model for link lengths below 1 km, a modification to the distance factor denoted (Ify) proposed by the ITUR was implemented as follows:
(7)
Where: f = frequency, d = link length, R0.01 = rainfall rate for 99.99% availability.
Hence to improve the performance of the ITUR P.53017 model for shortrange terrestrial link communications, the parameter Ify proposed in equation (7) is recommended. The attenuation for 0.01% of time is then given as:
dB (8)
Where specific attenuation kR0.01 is obtained from the procedure in Equation (5) and d is the link length.

ITUR P.53017 model
The model described in [14] is based on a distance factor denoted r which relies on the point rain rate (R0.01), frequency, link length, and powerlaw relationship coefficients of the specific attenuation are given as follows:
(9)
(10)
Where: f, and d are the frequency and actual path length respectively. With given as 1.1825 from [18]. The attenuation for 0.01 % of time is given as:
dB (11)
Where: deff is the effective path length given as:
(km) (12)
is obtained from Equation (5).
Attenuation for other percentages of time p for the range 0.001% to 1% is given by the following power law:
(13)
Where:
(14)
(15)
(16)

Abdulrahman model
The model proposed by Abdulrahman [19] is based on the differential approach. According to the model, attenuation is given as follows:
(17)
p
p
Where: kR% is the specific attenuation for p percentage of time obtained from ITUR recommendations. d is the link distance and deff is the effective path length through rain.


RESULTS AND DISCUSSION
In this section, the results from the performance of the four
(4) models described in section two are presented and discussed. The percentage of time considered ranges between
0.001 % to 1 %.
Fig. 1a Rain attenuation @ 100 mm/h
Fig.1d Rain attenuation @ 40 mm/h
Fig. 1b Rain attenuation @ 80 mm/h
The results from Figures 2(a) and 2(b) show that for 0.001% unavailability, the modified ITUR model overestimates attenuation due to rain by 5.613 dB and 5.4179 dB respectively. All models underestimated attenuation for 0.01% of time as attenuation ranged from 9.2587 dB to 32.628 dB across all models. From Figures 2(c), 2(d) and 2(e), all models underestimate attenuation for 0.001% to 1% unavailability for reference rain rates of 60 mm/h, 40 mm/h and 20 mm/h respectively. Figures 2(a) suggest that for 1% of time, all three models predict attenuation with similarity although an underestimation value of 4.12 dB was realized for the Modified ITUR and ITUR model. The Budalal model however underestimated attenuation by 4.299 dB.
Fig.1c Rain attenuation @ 60 mm/h
Fig.1e Rain attenuation @ 20 mm/h
An underestimation of attenuation by 21.761 dB and 24.718 dB was recorded for the ITUR and Budalal model respectively at 0.001% of time. The modified ITUR model however overestimated attenuation by 5.417 dB. Figure 2(b) further suggests attenuation ranging from 2.8178 dB to 4.615 dB for a system design of 8.77 hours a year across all models. An attenuation of 0.8017 dB going up to 0.9627 dB across all three models is estimated for a system design with 99% link availability. From Figure 2(c), all three models suggest that for a system design of 5.26 minutes of outage in a year, attenuation values ranging from 10.6293 dB to 33.3658 dB is expected. This comes with an underestimation value of at least 24.1967 dB. For time availabilities of 0.01% to 1% attenuation ranged from 0.5789 dB to 6.653 dB across all models. It was also observed from Figure 2(d) that the attenuation due to rainfall recorded a maximum value of 21.9838 dB for 99.999% availability and a minimum value of 6.7175 dB at 99.999% availability based on the modified ITUR and Budalal models respectively. The ITUR model however predicted attenuation of 8.6636 dB for a system design of 5.26 minutes of outage in a year. For 0.01% unavailability, the modified ITUR and ITUR model performed better than the Budalal model as both models underestimated attenuation by 8.851 dB. The Budalal model however underestimated attenuation by 9.836 dB. For 0.1% to 1% of time, all three models predicted attenuation due to rain ranging from 0.3658 dB to 2.2621 dB. Figure 2(e) shows that for 0.001% of time the modified ITUR model predicted
attenuation of 10.7686 dB which was the maximum attenuation predicted across all models. The ITUR and Budalal model however predicted attenuation of 4.2438 dB and 3.0655 dB respectively. An attenuation of 0.16695 dB to 2.1473 dB across all models was realized for 0.01% to 1% of time.
Table 2. Percent error for rain rate (R) = 20 mm/h
% of time
% error ITUR model
% error Modified ITUR model
% error Budalal model
0.001
68.63
20.40
77.34
0.01
71.03
71.03
79.07
0.1
61.68
47.73
72.32
1
87.12
87.12
90.69
The accuracy of the modified ITUR rain attenuation prediction model is tested by the root mean square value (RMS) and the percentage error measurement [20], [21], [22],
[23], [24], [25], [26]. The percentage error and the root mean square error are adopted to determine the measured error between the existing ITUR model, Budalal model, and modified ITUR model. The percentage error can be calculated by the following equation:(18)
Where:: Apredicted is the rain attenuation predicted and Ameasured is the rain attenuation measured at a given percentage of time. The root mean square (RMS) value can be estimated by using the mean error Âµe and standard deviation e. It is given as follows:
(19)
Table 3. Percent error for rain rate (R) = 40 mm/h
% of time
% error ITUR model
% error Modified ITUR model
% error Budalal model
0.001
69.47
22.53
76.32
0.01
66.87
66.87
74.31
0.1
48.10
29.22
59.76
1
79.00
79.00
83.71
The comparison of the rootmeansquare values for percentage of time ranging from 0.001% to 1% unavailability is shown in Table 2 to Table 6. Comparison is made between the Budalal model, modified ITUR model, and the existing ITUR model. From Table 2, the percentage error value of the existing ITUR model for 0.001% of time is 68.6313%. This resulting percentage error value is slightly higher and is not preferable for tropical climates. However, from the calculations, the percentage error of the modified ITUR model for 0.001% of time is 20.4031%. Hence, it can be
shown that the modified ITUR model significantly reduced the percentage error. For 99.99% link availability, both the ITUR and modified ITUR models estimated similar percentage errors of 71.0336%. The Budalal model however estimates a higher percentage error of 79.0765%. Results further suggest that for 0.1% unavailability, the maximum percentage error realized was 72.32% while the minimum percentage error was 47.7332%. For 99% link availability, the minimum percentage error was 87.1235% while the maximum percentage error obtained was 90.6988% across all models.
Table 4. Percent error for rain rate (R) = 60 mm/h
% of time
% error ITUR model
% error Modified ITUR model
% error Budalal model
0.001
62.24
4.19
69.47
0.01
65.89
65.89
72.43
0.1
56.02
40.02
64.45
1
81.50
81.50
85.45
From Table 3, it is shown the ITUR model and modified ITUR model's performance for a system design of 99.99% and 99% availability was the same. The percentage error of – 66.8789% and 79.0009 was realized for 0.01% and 1% of time respectively. However, the Budalal model estimated a higher percentage error for time availabilities of 99 to 99.999% with percentage error ranging from 76.3285% to – 83.7181%. Table 4, Table 5, and Tables 6 show that both the ITUR and modified ITUR model account for similar percentage errors for 99.99% availability for rain rates of 60 mm/h, 80 mm/h, and 100 mm/h respectively, while the modified ITUR performed best for 99.999% availability when compared to the other two models as shown in Table 5.
Table 5. Percent error for rain rate (R) = 80 mm/h
% of time
% error ITUR model
% error Modified ITUR model
% error Budalal model
0.001
55.17
13.73
62.67
0.01
64.07
64.07
70.08
0.1
65.35
52.73
71.14
1
78.02
78.02
81.70
Table 6. Percentage error for rain rate (R) = 100 mm/h
% of time
% error ITUR model
% error Modified ITUR model
% error Budalal model
0.001
65.21
11.04
62.70
0.01
72.58
65.51
70.61
0.1
80.94
66.83
79.27
1
81.94
77.28
80.64
Table 7. Root Mean Square value for selected models
% error ITUR model
% error Modified ITUR model
% error Budalal model
20
72.91
63.58
80.23
40
67.12
56.66
74.21
60
67.29
58.62
82.69
80
66.32
60.86
71.83
100
75.50
64.26
73.78
RMS values obtained from Table 7 suggest that the RMS of the existing ITU model was 75.504 for rain rate value 100 mm/h. The resulting RMS value has been reduced by the modified ITUR model to 64.2617. Results further suggest that for rain rates ranging 20 to 100 mm/h, the modified ITU R model reduced the RMS error value by at least 5.46135%.

CONCLUSION
In this study, a modification to the existing ITUR P.53017 rain attenuation prediction technique has been developed. Based on the results, it can be concluded that the modified ITUR P.53017 rain attenuation model has significantly improved the prediction error of the existing ITUR model. The new model is now suited for tropical locations and has been modified based on measured data and characteristics of rain in a tropical climate. The modified model can be implemented in different frequency bands and elevation angles for the prediction of attenuation due to rain. The new model can aid network engineers in the planning of fade mitigation techniques such as power control and site diversity.
ACKNOWLEDGMENT
The authors wish to thank the Nigerian Multilink's Telecommunications Center (NMTC) for making their facilities available for this research.
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BIOGRAPHIES OF AUTHORS
Joseph Mom received his BEng in Electrical and Electronics Engineering from Joseph Sarwuan Tarka University, Makurdi, Nigeria in 2004, he also received his MEng and Ph.D. Degree in Electronics Engineering from the University of Nigeria, Nsukka in 2009 and 2015 respectively. He is currently an associate Professor in the Department of Electrical and Electronics Engineering, Joseph Sarwuan Tarka University, Makurdi. He has published many articles in reputable journals and conference proceedings. He is a registered Engineer with the Council for the Regulation of Engineering in Nigeria (COREN) and also a member of the IEEE and ACM.
Soo Tyokighir holds BEng and MEng degree in Electrical and Electronics Engineering from the Joseph Sarwuan Tarka University, Makurdi in 2013 and 2019. He is currently a Ph.D. student and research assistant at Joseph Sarwuan Tarka University, Makurdi. He is a member of the Space & Satellite Professionals International, USA (SSPI), IEEE, Nigerian Society of Engineers (NSE) and COREN. His research interests are in engineering physics, radio communication and RF technologies.
Gabriel Igwue holds a B.Sc from the University of Lagos in 1973; M.Sc from the Massachusetts Institute of Technology, USA and a Ph.D from the North Caroline State University, USA, all in Electrical Engineering in 1975 and 1982 respectively. Professor Igwue has taught Electrical Engineering courses at both polytechnic and University for over thirty years. He has held many management positions in the public and private sectors. He is a COREN registered Engineer and a member of the Nigerian Society of Engineers (NSE). He has written several books in Electrical Engineering including Circuit Theory; Electrical Engineering Materials; Mathematical Methods for Engineers; Principles of Communications and Basic Circuit Theory and Industrial Electronics for physicists. He is married with children.