International Journal of Interactive Mobile Technologies(iJIM) – eISSN: 1865-7923 – Vol 16 No 11 (2022) Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of Rain Rate with Delay and Synthetic Storm Technique https://doi.org/10.3991/ijim.v16i11.30103 Md Moktarul Alam1, M. R. Hasan1, Md Rafiqul Islam1(), Mohamed Hadi Habaebi1, Ahmed Basahel1, Mandeep Singh2 1Electrical & Computer Engineering Department, Faculty of Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia 2Space Science Centre, Universiti Kebangsaan Malaysia, Selangor, Malaysia rafiqual_i089367@rediffmail.com Abstract—Future satellite companies will use higher Ka and V bands. Rain fade is the most important issue in establishing reliable communication between Earth and satellites outside the 10 GHz band. The problem is exacerbated in the tropics by significant rainfall throughout the year. One way to deal with rainfall attenuation is to use a time-diversity strategy. Real-time rainfall attenuation data is needed to analyze time variability. However, data from higher frequency bands such as Ka and V bands cannot be used. As a result, the Synthetic Storm Tech- nique (SST) was proposed to convert the measured real time rainfall data into rainfall attenuation data and predict the time diversity gain. The measured rainfall data was converted to rainfall attenuation data using the traditional SST method. Time diversity gains were predicted using Converted rainfall attenuation and the Matriciani model, and the measurements were significantly overestimated. A new concept of realtime rainfall with and without time delay is proposed and used to predict time diversity gain using ITU-R P. 618-13 and the measured rain- fall distribution with delay. Therefore, the proposed method recommends using the measured long-term precipitation data to predict the gain from time diversity at the desired frequency. Keywords—rain rate, rain attenuation, Synthetic Storm Technique (SST), time diversity gain, earth-to-satellite link, ITU-R P.618-13 1 Introduction In the future, satellite communication will shift to the Ka and V bands, eliminating the utilization of lower frequencies like the C and Ku bands [1]. The existing satellite communication system is dependent on bandwidth and data transmission [2]. Rain, cloud, precipitation, fog, and other environmental propagation deprivations, for exam- ple, have a significant impact on higher frequencies. Rain, on the other hand, makes it challenging to methods have been proposed satellite links at higher frequencies [3]. 178 http://www.i-jim.org https://doi.org/10.3991/ijim.v16i11.30103 mailto:rafiqual_i089367@rediffmail.com Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… Rain fade is very severe in tropical areas, thus numerous mitigation measures have been proposed to mitigate it, all of which have demonstrated promising results [4]. Since it may be a cost-effective and productive technique for combating rain con- striction, the time differing qualities approach is one of the reasonable support method- ologies [5], It’s moreover utilized in fawning communication to enhance performance over a corrupting connect [6]. Within the existing arrange network, measured rain attenuation information isn’t accessible in higher recurrence bands, whereas measured rain rate information is accessible in numerous locales [7]. By changing SST rain rate information into comparable watched rain constriction information, the transient dif- fering qualities for higher recurrence groups may be expanded. SST was built from information collected in tropical environments [8]. Change over the rainfall time series taken by rain intensity at a point during the 1-minute integration period into a longitudinal rainfall series along the line using the estimate of the synthetic storm method used to offset time with distance as shown in equation (1) [9] is a striking example of the basis of the SST established by [10]. SST is a powerful method for calculating time series of rainfall attenuation and long-term probability, daily and service-oriented statistics of reasonable rainfall attenuation [11]. SST not only improves the performance of satellite communication systems, but also makes designs more realistic [12]. Convolution is a mathematical method for solv- ing problems (16). In addition, for time compensation according to distance, the pre- cipitation time series obtained by the rain sensor during the 1-minute integration period in the precipitation spatial series along the line using the synthetic rainfall method esti- mation is as equation (1). SST is a effective method for calculating time series of rainfall attenuation and long-term probability, daily and service-oriented statistics of reasonable rainfall attenuation [11]. SST not only improves the performance of satellite communication systems, but also makes designs more realistic [12]. Convolution is a mathematical method for solving problems (1–6). A X K R X X K r R X dA L B L LA A B B A B( ) � � � �� � 0 0 0 0 � � �( ) ( ), ,� � � (1) When it rains, the functional structure of the troposphere is divided into two levels. A is the rainfall layer and B is the molten layer. It can be seen from equation (2). For satellite paths, signal attenuation is determined by the formula: A t K R t L r K R t L LA A B A BA A B( ) ( ) ( )( )� � � � � � (2) The path distance and specified rain attenuation are denoted by A(X). KA represents raindrops at 200C, whereas KB represents raindrop size at 0 0C. The highest limit of layer A’s height above sea level is designated by the letter HA. In equations (3) and (4), it is expressed as HA = HB - h km, where h is the melting layer thickness. The formulae LA = (HA – HS)/sin(θ) and LB = (HB – HS)/sin(θ) are used to cal- culate the radio path lengths LA and LB, where θ is the link of elevation angle and HS is the Earth station’s height above sea level, respectively. Δx0 = ΔL cos(θ) = h/tan(θ) (3) iJIM ‒ Vol. 16, No. 11, 2022 179 Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… ΔL = LB – LA = h/sin(θ) (4) The equation generates a decaying time series that is correlated with a rainfall rate time series to produce a decaying data set (5). By transforming the Fourier transform theory into the decaying time series of equation (5) and making some assumptions, [15] gives the following equation: A t K R t L r K R t L LA A B B AA B B( ) ( ) ( )( )� � � � � � (5) The time series of rainfall intensity R(t), like the other parameters, was developed earlier. If the wind speed (v) is close to infinity and the theta angle is not 90 degrees, then this equation can be used. The rainfall attenuation probability distribution is calcu- lated from the probability distribution of the rainfall rate, excluding the additional SST in equation (6). A C K R C K R B LA B mA B� � �[ ( ) ( . ) ]0 01 3 134 � � (6) where C0 and L are constants in the given scheme and L (km) is the mean long-term slope track of precipitation according to [16]. The values of m used in the following equations are derived from equation (6), where K is a standard constant that converts the rainfall rates for the two precipitation layers A and B shown in the SST to a specific (dB/km) rainfall attenuation [17]. The rain rate statistic study for the Ku band utilizing the SST technology. Figures 2 and 3 show a comparison of rain attenuation using the SST approach to the measured one. It is clear that the SST technique exhibits the same characteristics as the mea- sured one [9]. In addition, the SST predicted attenuation is applied in the time diversity approach. For each event, the rainfall attenuation statistics for SST are nearly identical to the observed rainfall attenuation statistics [9]. Two peaks were found in Figure 1. SST estimates the main peak rainfall attenuation to be about 13 dB. At the second peak, the observed rain fade price is 10 dB and the SST price is 12.8 dB. The measured rain- fall reduction cost at the second peak is 11 dB, but SST estimates it to be around 11 dB. The 30-minute average rainfall attenuation for the first event is 2.755 decibels, which is 3.405 decibels according to the SST calculation. For the second case, the 48-minute mean of the rainfall attenuation time series recorded for the second case is 4.855 dB and predicts an SST of 5.329 dB. 180 http://www.i-jim.org Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… 12 12.05 12.1 12.15 12.2 12.25 12.3 12.35 12.4 12.45 12.5 Time 10 20 30 40 50 60 70 80 90 R ai n ra te (m m /h r) 0 5 10 15 20 25 A tt en ua tio n (d B ) Predicted Attenuation by using SST for Ku-band Measured rain rate for Ku-band Measured Attenuation for Ku-band Fig. 1. For a rainy event on August 16, 2012, there was a comparison between rain rate, recorded rain attenuation, and rain attenuation time series converted by SST [9] 2 Time diversity technique The time delay can range from 1 minute to 1 hour or more, and the same data is retrans- mitted each time. Temporal fluctuations set up a load on the receiver’s memory when a lot of processing time is required [17]. Estimate the time-variety performance using an additional cumulative time-delayed rainfall attenuation distribution function [18]. P(A) = P[A(t) > Ath, A(δt) > Ath] (7) While t is regarded a time diversity delay, and A(t) is the rain attenuation value that shifted of t, A(t) is the rain attenuation value that shifted of t. The following is a rewrite of equation (5): P(A) = P[min(A(t) > Ath, A(δt) > Ath)] (8) As a result, the minimal attenuation with variety [18] is as follows: ATD = min[A(t), A(δt)] (9) The main rainfall attenuation is 28.24 dB without time delay, but after a delay of 10 minutes to 30 minutes, the maximum rainfall attenuation is 24.35 dB and the minimum rainfall attenuation is 6.68 dB. Figure 2 shows the time diversity method for Earth satellite path signals for rainfall events detected in Malaysia [19–20]. iJIM ‒ Vol. 16, No. 11, 2022 181 Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… 17 17.2 17.4 17.6 17.8 18 18.2 18.4 Time 0 5 10 15 A tt en ua tio n (d B ) Signal without time delay Signal with time delay T = 30 Received signal after time delay Fig. 2. On the signal of a satellite-Earth link with a rainfall event detected in Malaysia by using time delay method [19,22] The difference in dB between the cumulative distributions of attenuation and their value with a set time delay is also known as gain of temporal diversity, and it may be represented statistically using the equation provided in (10). GTD = A(t) – ATD (10) where GTD is defined as the gain from time diversity, A(t) is the time decay due to rain, and ATD is the time diversity decay. Matricciani presented the following rain attenuation gain model based on his mea- surements in Italy: G e e Af T e A� � � � �( + )0 65 0 38 10 036 0 09 1 0 44. . ( ). . ( ). (11) where G is the attenuation factor, f is the frequency in GHz, T is the delay period, and A is the rainfall attenuation level. 3 Measurement setup For receiving beacon signal measurement, the rain rate and attenuation distribution, as well as its characteristics, were measured at the University Sains Malaysia (USM), (4.39°N, 100.98°E), at a height above mean sea level of 57 m and a distance of 4.86 km. At 12.255 GHz, with a 40.1° elevation angle, the SUPERBIRD-C signal was received [21,22]. For USM, data on 1-year rain rate and rain attenuation were collected throughout the course of 2009. 182 http://www.i-jim.org Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… 4 Result and analysis 4.1 Measured rain attenuation distribution with time delay The measured rain attenuation is analyzed using equations (7–9) to develop cumula- tive distribution function. The measured rain rate CDF without delay (T = 0) and with delays of 1, 3, 5, 10, 15, 20, 25 and 30 minutes are shown in Figure 3. 0 5 10 15 20 25 30 Rain attenuation (dB) 10–3 10–2 10–1 100 T im e ex ce ed an ce , % Without Time Delay With Time Delay = 1 With Time Delay = 3 With Time Delay = 5 With Time Delay = 10 With Time Delay = 15 With Time Delay = 20 With Time Delay = 25 With Time Delay = 30 Fig. 3. Measured rain attenuation for several time delays in at USM, Malaysia for the year 2009 by using complementary cumulative function Without the time diversity approach, the inverse relationship between time delay and excess attenuation for a given percentage of time is 23.6 dB. For time delays 1, 3, 5, 10, 15, 20, 25, and 30 minutes, the rainfall attenuation values are 20, 19, 2, 18, 1, 15. 8, 14.9, 14, 13.8 and 13 dB respectively. 4.2 Time diversity technique by using SST By following the rain attenuation time series by using SST equation (1–6), is used to make a comparison of the original time series of rain rate and measured rain atten- uation time series in Figure 4. It has been observed that pick in the rain rate and pick in the attenuation at 11 m/s are at the same time and it is closer to rain rate. As a result, for 1-year data conversion, a velocity of 11 m/s is used. Furthermore, the most rele- vant finding is that SST-based attenuation rises fast as a function of observed rain rate [23, 24]. This makes sense because the majority of rainfall in tropical places occurs in short bursts with high intensity. iJIM ‒ Vol. 16, No. 11, 2022 183 Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… 0.155 0.16 0.165 0.17 0.175 0.18 0.185 0.19 0.195 0.2 Time exceedance, % 10 20 30 40 50 60 70 80 R ai n ra te (m m /h r) 0 5 10 15 20 25 30 R ai n at te nu at io n (d B ) Measured Rain rate time series Measured rain attenuation time series Predicted rain attenuation time series by using SST Fig. 4. An example of one event measured rain rate, measured rain attenuation and convert attenuation by using SST Attenuation converted by SST and T is time regarding days based on a year. For getting the CDF, whole year predicted attenuation data of 2009 converted by the SST have been taken. The SST technique is implemented of time delay method by using equations (1–10). The rain attenuation CDF by converting SST technique without delay (T = 0) and with delays of 1, 3, 5, 10, 15, 20, 25 and 30 minutes are shown in Figure 5. 0 5 10 15 20 25 30 35 40 45 Rain attenuation (dB) 10–3 10–2 10–1 100 T im e ex ce ed an ce , % Without Time Delay With Time Delay = 1 With Time Delay = 3 With Time Delay = 5 With Time Delay = 10 With Time Delay = 15 With Time Delay = 20 With Time Delay = 25 With Time Delay = 30 Fig. 5. Cumulative distributions of predicted rain attenuation by using SST for several time delays Each curve indicates to a specific time delay represents predicted rain attenuation before applying the time diversity technique. In Figure 6 at 0.01 % attenuation with- out applying time delay technique is 44.8 where the rain attenuation values are 40.8, 39.6, 36.2, 34.3, 30, 26.4, 25 and 23.9 dB for 1, 3, 5, 10, 15, 20, 25 and 30 minute time 184 http://www.i-jim.org Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… delay respectively. It is obvious the difference of attenuation by using equation (10) is increasing with delay of time. Simulations using the Synthetic Storm Technique (SST) yielded temporal in non-real-time satellite communications in the frequency range 12 to 100 GHz, variety during rain attenuation [25–27]. Figure 6 presents a comparison of measured, SST, and Matricciani research results with a time delay of 1 to 30 minutes at 0.01 percent. All gain observations show that SST and Matricciani extrapolated gain was higher than measured gain at Ku band. 0 5 10 15 20 25 30 Time delay 0 5 10 15 20 25 30 35 A tt en ua tio n ga in (d B ) Matricciani predicted GA for Ku-band Predicted GA by using SST for Ku-band Measured GA for Ku-band Fig. 6. For 12 GHz, a comparison of SST calculated, Matricciani predicted, and observed gains was made [24] 4.3 Proposed time diversity technique based on ITU-R P.618-13 and rain rate with delay The rain rate with time delay is proposed based on the real time rain attenuation with time delay concept which are elaborated in equations (7–9). The complementary cumulative distribution function is proposed to be expressed on real time rain rate and the same period with time delay as considered for rain attenuation. The Rm(t) means the real time rain rate and R(t + T) means the rain rate with time delay where the time diversity delay is expressed T. The equation (1 to 3) are proposed based on rain rate time series as follows: P R P Rm t R Rm t dR R ( ) [ ( ) ] [ ( )]� � � � �� (12) Where x is the rain rate Rm(t) density function, and P(R) is its integral, com- puted for all time t in the rain rate time series. The proposed expression for the joint distribution is P Rm t R R t T R R t R t T dR t dR t T R R [ ( ) , ( ) ] [ ( ), ( )] ( ) ( )� � � � � � ��= � (13) iJIM ‒ Vol. 16, No. 11, 2022 185 Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… The joint probability density function g of Rm(t) and R(t + T) with delay time T is [Rm, t., R(t + T)]. The condition of rain rate with time delay method can be used as the complementary cumulative distribution function to eastimate the implemention of rain rate with time diversity as: RTD = min[R(t), R(t + T)] (14) The measured rain rate for the year 2009 at USM is analyzed using equations (12–14) to develop cumulative distribution function. The measured rain rate CDF with- out delay (T = 0) and with delays of 1, 3, 5, 10, 15, 20, 25 and 30 minutes are shown in Figure 7. For the environmental analysis It has been observed due to heavy rain fall in Malaysia signal becomes drop and heavy attenuation is occurred as a result lots of bar- rier occurred like economical lose, right information lose coming from the satellite. For the situation basis time delay can be effective with 1, 3, 5, 10, 15, 20, 30 minutes. It has been observed that most of the cases 30 minutes time delay is effective. 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Rain rate (mm/hr) 10–4 10–3 10–2 10–1 100 T im e ex ce ed an ce , % Without Time Delay With Time Delay = 1 With Time Delay = 3 With Time Delay = 5 With Time Delay = 10 With Time Delay = 15 With Time Delay = 20 With Time Delay = 25 With Time Delay = 30 Fig. 7. Cumulative distributions function of measured rain rate with time delays As a result, the Synthetic Storm Technique (SST) was proposed to convert the mea- sured realtime rainfall data into rainfall attenuation data and predict the time diversity gain. The measured rainfall data was converted to rainfall attenuation data using the traditional SST method. While the rain rate level values are 95, 90, 78, 70, 64, and 59 mm, with time delays of 1, 3, 5, 10, 15, 20, 25, and 30 minutes, the rain rate level values are 95, 90, 78, 70, 64, and 59 mm. The ITUR P.618-13 forecasting method is used to estimate rainfall attenuation in Earth-satellite links using measured rainfall rates with and without a time percentage overshoot of 0.01%. The satellite is assumed to be SUPERBIRDC with an elevation angle of 40.1°. All signals are treated as horizontally polarized and attenuation is pre- dicted using equation (15–17). 186 http://www.i-jim.org Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… To obtain the specific attenuation, gR using the frequency-dependent coefficients [15] gR = K(R0.01) a (15) k and α, these parameters are dependent on frequency, rain temperature, raindrop size distribution, and polarization are given in Recommendation ITU-R P.618-12 [16], In order to predict attenuation exceeded in 0.01% of an average year, the following formula is used. A0.01 = gRLE (16) Here LE is effective path length. The estimated attenuation to be exceeded for other percentages of an average year, in different percentage, is determined from the attenuation to be exceeded for 0.01% for an average year [15]. A A p p p A p � � � � � � � � � � � � 0 01 0 655 0 033 0 045 10 01 . ( . . ln( ) . ln( ). 0.01 � ( )) sin )� (17) Based on earth-to-satellite prediction model proposed by using ITU-R equation (11–13) [19] and using 0.01% rain intensities of R 0.01% = 102 mm/hr (T = 0), 95 mm/ hr (T = 1 min), 90 mm/hr (T = 5), 78 mm/hr (T = 10), 70 mm/hr (T = 15), 64 mm/hr (T = 20), 59 mm/hr (T = 25) and 53 mm/hr (T = 30) the predicted attenuation is shown in Figure 8. 0 5 10 15 20 25 30 Attenuation (dB) 10–3 10–2 10–1 100 101 T im e ex ce ed an ce , % Without Time Delay With Time Delay = 1 With Time Delay = 3 With Time Delay = 5 With Time Delay = 10 With Time Delay = 15 With Time Delay = 20 With Time Delay = 25 With Time Delay = 30 Fig. 8. Cumulative distributions of predicted rain attenuation by using ITU-R for several time delays Using the time diversity approach, Figure 8 illustrates the forecast rain attenuation distribution. For example, without using the time diversity approach, the rain attenua- tion level is 25.5 dB at 0.01-time percentage. While the rain attenuation level values are 24, 23.2, 22.8, 20.5, 19.2, 18.3, 17.2, and 16 dB for time delays of 1, 3, 5, 10, 15, 20, 25, and 30 minutes, respectively, with temporal variety. iJIM ‒ Vol. 16, No. 11, 2022 187 Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… 4.4 Time diversity rain attenuation gain The time diversity attenuation gain (GA) is predicted by using SST technique as in equations (1–10) and one year measured rain rate. The same is also predicted by Matricciani’s prediction model in equation (11). The time diversity attenuation gain is also predicted based on ITU-R P.618-13 as shown in equations (15–17) using new concept of measured rain rate with delay as in equations (12–14). The above three predicted gains are compared with those measured for the same period of time using equations (7–9) and shown in Figure 9. The most important observation is that the both predicted and measured GA increase rapidly as a function of time delay up to 30 minutes and after 30 minutes delay it is increasing gradually. 0 5 10 15 20 25 30 Attenuation gain (dB) 0 5 10 15 20 25 T im e Measured GA Predicted GA by Using ITU-R Predicted GA by Using SST Predicted GA by Using Matricciani Fig. 9. Comparison among measured rain attenuation gain, predicted rain attenuation gain by using SST, predicted rain attenuation gain by using SST and Matricciani model gain at 0.01% for 12 GHz The predicted GA by SST Method is compared with measured GA and found that after it overestimates from measured GA. The predicted GA is 10.4 dB higher than mea- sured one for 30 minutes delay which indicates above 51% error. The predicted GA by Matricciani using equation () is compared with measured GA and found that after it overestimates from measured GA. The predicted GA is 9.2 dB higher than measured one for 20 minutes delay which indicates above 48% error. From Figure 9, it is obvious that gain predicted by proposed method is almost simi- lar character with measured gain. It underestimates and maximum 0.8 dB difference is found at delay of 15 minutes which indicates 12% of error. The comparison of predictions by using SST, Matricciani model and proposed method, the proposed method is found very good agreement with data measure- ment. And it shows the performance much better than SST and model proposed by Matricciani. Hence the proposed method will be useful in future for modelling of time diversity technique at any higher frequencies by using measured rain rate. 188 http://www.i-jim.org Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… As a result, rain rate with delay can be assumed to represent rain attenuation with delay for the same amount of time at the same place. This assumption is true as long as rain induces attenuation. As a result, it is clear that the time diversity approach may be assessed using a new concept of rain rate, which has been defined as the difference in rain rate with and without time delay, with substantial accuracy, as opposed to being assessed just on the basis of recorded rain attenuation. This notion demonstrates how recorded rain rate may be used to determine attenuation gain at any higher frequency. The following relationship may be used to build the suggested temporal diversity gain model for rain attenuation: GATD%p = f (Rm%p, T, freq, El) (18) Where Rm%p = measured rain rate at %p T = time delay Freq = frequency of link El = elevation angle of link A slight discrepancies in proposed method with measured attenuation gain in Figure 9 may be caused by the elevation angle considered in ITU-R prediction method [13,14]. 5 Conclusion Earth-to-satellite communication technologies are going towards higher and higher frequency bands in the future. Rain fading is the most difficult aspect of developing extremely dependable earth-to-satellite communications above 10 GHz. The problem is exacerbated in tropical areas by the fact that significant rainfall occurs throughout the year. Time variety is considered a cost-effective and efficient mitigation approach to counterbalance the attenuation produced by rain. The improvement in temporal diversity attenuation is thought to be a prerequisite for future high-frequency networks. Synthetic storm approach and Matricciani’s model based on recorded rain rate are both approved internationally for predicting this gain. The rain rate and rain attenuation data recorded at Ku-band for a year in Malaysia are presented in this article. Using the SST approach, the recorded rain rate data was converted to rain attenuation data. The tempo- ral diversity gain was predicted using SST converted rain attenuation and Mattriciani’s model, and the measurement was found to be greatly overstated, with an inaccuracy of up to 50%. The ITU-R P.618-13 and observed rain rate distributions with delay were used to estimate time diversity gain utilizing a new concept of real-time rain rate with and without time delay. With a 12% inaccuracy, the estimated gain is shown to be close to observations. As a result, utilizing recorded long-term rain rate data, the suggested technique is recommended for more correctly predicting temporal diversity gain at any required frequency. iJIM ‒ Vol. 16, No. 11, 2022 189 Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… 6 References [1] S. L. Jong, M. D’Amico, J. Ding, and H. Y. Lam, “Performance of time diversity technique in heavy rain region,” In 2014 International Symposium on Antennas and Propagation Confer- ence Proceedings IEEE, pp. 575–576, 2014. https://doi.org/10.1109/ISANP.2014.7026782 [2] P. M. Arapoglou, A. D. Panagopoulos, and P. G. Cottis, “An analytical prediction model of time diversity performance for earth-space fade mitigation,” Int. J. Antennas Propag., vol. 2008, 2008. https://doi.org/10.1155/2008/142497 [3] A. M. Al-Sayegh, A. Sali, J. S. Mandeep, and A. Ismail, “Journal of Atmospheric and Solar-Terrestrial Physics Extracted atmospheric impairments on earth-sky signal quality in tropical regions at Ku-band,” J. Atmos. Solar-Terrestrial Phys., vol. 104, pp. 96–105, 2013. https://doi.org/10.1016/j.jastp.2013.08.018 [4] E. Matricciani and C. Riva, “18.7 GHz tropospheric scintillation and simultaneous rain attenuation measured at Spino d’Adda and Darmstadt with Intelsat,” Radio Sci., vol. 43, no. 1, pp. 1–13, 2008. https://doi.org/10.1029/2007RS003688 [5] H. Y. Lam, L. Luini, J. Din, C. Capponi, and A. D. Panagopoulos, “Performance of SatCom systems implementing time diversity in equatorial Malaysia,” In The 8th European Conference on Antennas and Propagation (EuCAP 2014), pp. 511–514, 2014. https://doi. org/10.1109/EuCAP.2014.6901805 [6] E. Matricciani, “Time diversity as a rain attenuation countermeasure in satellite links in the 10-100 GHz frequency bands,” In 2006 First European Conference on Antennas and Propa- gation, pp. 1–6, 2006. https://doi.org/10.1109/EUCAP.2006.4584760 [7] Y. Ng, M. Singh, J. Singh, and V. Thiruchelvam, “Performance analysis of 60-min to 1-min integration time rain rate conversion models in Malaysia,” J. Atmos. Solar-Terrestrial Phys., vol. 167, pp. 13–22, January 2018. https://doi.org/10.1016/j.jastp.2017.10.004 [8] N. W. M. Saad, A. F. Ismail, K. Badron, and F. Isa, “Proposed rain fade mitigation technique for Ka-band space-Earth links in tropical climate,” In 2013 IEEE 11th Malaysia Interna- tional Conference on Communications (MICC), pp. 511–516, 2013. https://doi.org/10.1109/ MICC.2013.6805883 [9] A. K. Lwas, M. R. Islam, J. Chebil, M. H. Habaebi, A. F. Ismail, A. Zyoud, and H. Dao, “Rain attenuation analysis using synthetic storm technique in Malaysia,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 53, no. 1, 012045, 2013. https://doi.org/10.1088/1757-899X/53/1/012045 [10] P. Chodkaveekityada and H. Fukuchi, “Prediction model of time diversity using Japan rain radar data,” International Journal of Satellite Communications and Networking, 2016. https://doi.org/10.1002/sat.1182 [11] E. Matricciani, “Physical-mathematical model of the dynamics of rain attenuation based on rain rate time series and a two-layer vertical structure of precipitation,” Radio Sci., vol. 31, no. 2, pp. 281–295, 1996. https://doi.org/10.1029/95RS03129 [12] C. I. Kourogiorgas, A. D. Panagopoulos, S. N. Livieratos, and G. E. Chatzarakis, “Time diversity prediction modeling using Copula functions for satellite communication systems operating above 10GHz,” In 2014 XXXIth URSI General Assembly and Scientific Sympo- sium (URSI GASS), pp. 1–4, 2014. https://doi.org/10.1109/URSIGASS.2014.6929673 [13] ITU-R Recommendation P.618-13, “Propagation data and prediction methods required for the design of Earth-space telecommunication systems,” Rec. ITU-R P.618-13, vol. 13, no. December, 2017. [14] ITU-R, “ITU-R P.838-2 Specific attenuation model for rain for use in prediction methods,” pp. 1–5, 2003. 190 http://www.i-jim.org https://doi.org/10.1109/ISANP.2014.7026782 https://doi.org/10.1155/2008/142497 https://doi.org/10.1016/j.jastp.2013.08.018 https://doi.org/10.1029/2007RS003688 https://doi.org/10.1109/EuCAP.2014.6901805 https://doi.org/10.1109/EuCAP.2014.6901805 https://doi.org/10.1109/EUCAP.2006.4584760 https://doi.org/10.1016/j.jastp.2017.10.004 https://doi.org/10.1109/MICC.2013.6805883 https://doi.org/10.1109/MICC.2013.6805883 https://doi.org/10.1088/1757-899X/53/1/012045 https://doi.org/10.1002/sat.1182 https://doi.org/10.1029/95RS03129 https://doi.org/10.1109/URSIGASS.2014.6929673 Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… [15] A. K. Lwas, M. R. Islam, M. H. Habaebi, S. J. Mandeep, A. F. Ismail, and A. Zyoud, “Effects of wind velocity on slant path rain-attenuation for satellite application in Malaysia,” Acta Astronaut., vol. 117, pp. 402–407, 2015. https://doi.org/10.1016/j.actaastro.2015.09.008 [16] E. Matricciani and P. Milano, “Time diversity in satellite links affected by rain: Prediction of the gain at different localities,” 1982. [17] ITU-R, “Rain height model for prediction methods,” Recomm. ITU-R P.839-3, vol. 3, pp. 1–3, 2001. [18] H. Fukuchi and P. Chodkaveekityada, “Propagation impairments along satellite-to-earth path and their mitigation technologies,” In 2015 IEEE 4th Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 433–434, 2015. https://doi.org/10.1109/ APCAP.2015.7374437 [19] E. Matricciani, “Probability distributions of rain attenuation obtainable with linear combin- ing techniques in space-to-Earth links using time diversity,” Int. J. Satell. Commun. Netw., vol. 36, no. 2, pp. 220–237, March/April 2018. https://doi.org/10.1002/sat.1214 [20] M. Alam, I. Rafiqul, and K. Badron, “Time diversity gain analysis for earth to satellite link based on measured rain rate,” In 2018 7th International Conference on Computer and Communication Engineering (ICCCE), no. 1, pp. 109–113, 2018. https://doi.org/10.1109/ ICCCE.2018.8539317 [21] H. Dao, M. R. Islam, and K. Al-Khateeb, “Modification of ITU-R rain fade slope prediction model based on satellite data measured at high elevation angle,” IIUM Eng. J., vol. 12, no. 5, pp. 53–59, 2011. https://doi.org/10.31436/iiumej.v12i5.233 [22] S. A. Zabidi, M. R. Islam, W. Al-Khateeb, and A. W. Naji, “Analysis of rain effects on ter- restrial free space optics based on data measured in tropical climate,” IIUM Eng. J., vol. 12, no. 5, pp. 45–51, 2011. https://doi.org/10.31436/iiumej.v12i5.232 [23] M. M. Alam, I. M. Rafiqul, K. Badron, F. D. AR, H. Dao, M. R. Hassan, and A. K. Lwas, “Investigation of time diversity gain for earth to satellite link using rain rate gain,” Bull. Electr. Eng. Inform., vol. 8, no. 3, https://doi.org/10.11591/eei.v8i3.1512 [24] A. F. Ismail and P. A. Watson, “Characteristics of fading and fade countermeasures on a satellite-Earth link operating in an equatorial climate, concerning broadcast applications,” IEE Proc. – Microwaves, Antennas Propag., vol. 147, no. 5, p. 369, 2000. https://doi. org/10.1049/ip-map:20000704 [25] M. R. Hassan, M. R. Islam, M. H. Habaebi, M. M. Alam, K. Badron, and A. Basahel, “Time diversity analysis based on predicted rain attenuation at Ku, Ka and V-Bands using syn- thetic storm technique,” In 2021 8th International Conference on Computer and Commu- nication Engineering (ICCCE), pp. 401–405, June 22–23, 2021. https://doi.org/10.1109/ ICCCE50029.2021.9467239 [26] S. Ishii, “Rain attenuation at millimeter wavelength of 1.33 mm,” Int. J. Infrared Millim. Waves, vol. 25, no. 10, pp. 1495–1501, 2004. https://doi.org/10.1023/B: IJIM.0000047441.92970.e8 [27] S. Dhamodaran and M. Lakshmi, “Ensampling data prediction using sparse data in mobile intelligent system,” Int. J. Interact. Mob. Technol., vol. 13, no. 10, pp. 106–119, 2019. 7 Authors Md Moktarul Alam received his both Bachelor’s and Master’s degrees in Electrical and Electronics Engineering. His research interests are in the fields of Antenna and Wave Propagation. E-mail: moktaruleee@gmail.com iJIM ‒ Vol. 16, No. 11, 2022 191 https://doi.org/10.1016/j.actaastro.2015.09.008 https://doi.org/10.1109/APCAP.2015.7374437 https://doi.org/10.1109/APCAP.2015.7374437 https://doi.org/10.1002/sat.1214 https://doi.org/10.1109/ICCCE.2018.8539317 https://doi.org/10.1109/ICCCE.2018.8539317 https://doi.org/10.31436/iiumej.v12i5.233 https://doi.org/10.31436/iiumej.v12i5.232 https://doi.org/10.11591/eei.v8i3.1512 https://doi.org/10.1049/ip-map:20000704 https://doi.org/10.1049/ip-map:20000704 https://doi.org/10.1109/ICCCE50029.2021.9467239 https://doi.org/10.1109/ICCCE50029.2021.9467239 https://doi.org/10.1023/B:IJIM.0000047441.92970.e8 https://doi.org/10.1023/B:IJIM.0000047441.92970.e8 mailto:moktaruleee@gmail.com Paper—Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of… M. R. Hasan received his both Bachelor’s and Master’s degrees in Electrical and Electronics Engineering. Currently he is working for PhD in Electrical Engineering. His research interests are towards the fields of propagation measurement and 5G channel modeling. Md Rafiqul Islam professor with the department of Electrical and Computer Engi- neering, International Islamic University Malaysia. His research interests are in wire- less channel modeling, radio link design, RF propagation measurement and modeling in tropical and desert, RF design, smart antennas and array antennas design, FSO prop- agation and modeling etc. E-mail: rafiq@iium.edu.my Mohamed Hadi Habaebi is a professor with the department of Electrical and Com- puter Engineering, International Islamic University Malaysia. His research interests are in IoT, wireless communications, small antenna design, wireless channel propagation, FSO propagation and Networking. E-mail: habaebi@iium.edu.my Ahmed Basahel received his BSc, MSc and PhD degrees in Electrical Engineering. His research interests are towards the fields of Free Space Optics (FSO) propagation measurements and modelling. Email: ba_sahal@hotmail.com Prof. Ir. Dr. Mandeep Singh A/L Jit Singh is the head of Angkasa in the National University of Malaysia. His research interests are satellite communication, propagation measurements and modelling, antenna and RF design. Email: mandeep@ukm.edu.my Article submitted 2022-02-05. Resubmitted 2022-04-08. Final acceptance 2022-04-09. Final version published as submitted by the authors. 192 http://www.i-jim.org mailto:rafiq@iium.edu.my mailto:habaebi@iium.edu.my mailto:ba_sahal@hotmail.com mailto:mandeep@ukm.edu.my