RAIN FADE ANALYSIS AT C, Ku AND Ka BANDS IN NIGERIA
BY
SANYAOLU, MODUPE EUNICE
PHY/14/5847
A DISSERTATION SUBMITTED IN THE DEPARTMENT OF PHYSICAL SCIENCES
TO THE SCHOOL OF POSTGRADUATE STUDIES, REDEEMER’S UNIVERSITY
EDE, OSUN STATE, NIGERIA
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF THE
DEGREE OF MASTERS OF SCIENCE (M. Sc) IN COMMUNICATION PHYSICS
JULY, 2016
CERTIFICATION
This is to certify that this research work was carried out by Sanyaolu, Modupe Euniceof the
Department of Physical Sciences, Redeemer’s University, Ede, Osun state.
…………………………… …………………………
Prof.L.B Kolawole Date Supervisor
DEDICATION
This work is dedicated to my beloved husband, Mr. Sanyaolu Olufemi Oluseunand my children;
Ayomide, Anjolaoluwa and Temiloluwa.
ABSTRACT
Telecommunication systems are rapidly moving to higher frequencies due to the congestion at the lower frequency bands. In the design of telecommunication systems, the dynamic characteristics of fading due to atmospheric effects are essential in order to optimize system capacity and meet quality and reliability of signal reception.
This study pertains to the analysis of rain fades at C, Ku and Ka bands at some selected stations covering the main geographical zones of Nigeria.The propagation model recommended by the
International Telecommunication Union (ITU-RP) was used to calculate the fade depth at 6 GHz,
8 GHz, 12 GHz, 16 GHz, 20 GHz, 30 GHz and 40 GHz.
The result shows that the higher the frequency, the higher the fade depth, andit is most severe in
Port Harcourt, followed in descending order, by Lagos, Nsukka, Akure, Yola, Minna and
Ayingba. Port Harcourt has the highest rain rate and was seen to have the highest fade depth.
The rain fade correlate with signal attenuation. Attenuationdistribution for a percentage of time unavailability and availability were estimated. The results therefore show thatthe attenuations for vertically and circularly polarized signal are less than that of the horizontal polarization at all the frequencies.This shows that rain fade is less severe in the Northern part of the country and is more severe in the southern part of Nigeria, with Port Harcourt, Lagos and Nsukka experiencing the highest rain impairment. Attenuation due to effective path lengths is of little effect due to other dominant factors such as frequency and local rain rate.
The fade durations showing the number of events for which duration exceeds threshold at 1, 3, 6,
9, 12, 15 and 18 dB levelswere also determined. It isseenthat when the attenuation is increased the fade duration decreases.
ACKNOWLEDGEMENTS
I appreciate the Almighty God for seeing me through this course. He is my help and strength. It has been His grace all the way.
I wish to express my profound gratitude to Professor L.B. Kolawole for his fatherly love.He encouraged me and thoroughly supervised this work.
The support of the National Space Research and Development Agency (NASDRA) is gratefully acknowledged for establishing The NigeriaEnvironmental and Climatic Observatory Programme
(NECOP)and for donating the equipment used for this study.
My sincere appreciation also goes to Dr. E.U. Vincent, Dr. Falade, Engr. Dairo, Mr Osinowo,
Mr Akinyemi and every other member of staff of the Department of Physical Sciences.
I wish to show my sincere appreciation to the management of Redeemer’s University for the staff development scholarship offered me for this Master’s degree programme.
I am expressing my deep gratitude to God on behalf of my late father Mr L.A. Owoseni. Daddy, your children are doing well, and may your soul rest in peace. I want to say a big thank you to my sweet mother, Mrs Emily Owoseni and my siblings- Mrs Bola Ogunniyi, Kayode, Seun, and
Tosin Owoseni for their love and support. Many thanks also to my in laws: Daddy and Mummy
Sanyaolu.
I appreciate my husband Olufemi Sanyaolu and our God’s gifts; Ayomide, Anjolaoluwa and
Temiloluwa, for their love, prayers, understanding, support and encouragement during the period of this work.
TABLE OF CONTENT
Certification i
Dedication ii
Abstract iii
Acknowledgements iv
Table of contents v
List of Figures vii
List of Tables ix
List of Abbreviation x
CHAPTER ONE:
1.0 Introduction 1
1.1 Background of study 1
1.2 Statement of the problem 13
1.3 Objectives 13
1.4 Scope of study 14
CHAPTER TWO
2.0 Literature review 15
2.1 Raindrop size and shape 18
2.2 Rain rate prediction models 18
2.3 Rain fade mitigation techniques 22
2.4 Fixed wireless link power budget 26
2.5 Rain attenuation models 27
2.6 ITU-R prediction of rainfall over tropical region 28
2.7 Rain height and noise temperature 30
2.8 Fade depth prediction 30
2.9 Fade duration distribution models 31
CHAPTER THREE
Materials and methodology
3.0 Equipment 33
3.1 Instrumentation 34
3.2 Data 36
3.3 Rainfades calculations 38
CHAPTER FOUR
4.0 Data analysis 49
4.1 Results 51
CHAPTER FIVE
5.0 Discussion of Results 78
5.1 Conclusion and Recommendation 82
References 84
LIST OF FIGURES
Figure 1.1 Features characterizing the dynamic of fade events 9
Figure 1.1 Map of Nigeria showing study areas 14
Figure 3.1 The TRODAN station at the Redemption Camp, Mowe,
Ogun State 34
Figure 3.2 The locations where data were taken for this work 37
Figure 3.3 A schematic diagram of slant range below freezing point 39
Figure 3.4 A schematic diagram of earth-space path 42
Figure4.1 The average monthly rainfall accumulations during the
observation period 50
Figure 4.2 Rain rate for the eight stations in Nigeria 52
Figure 4.3 Rain rate for Mowe and Minna 53
Figure 4.4 Rain rate for Lagos and Nsukka 54
Figure 4.5 Rain rate for Port Harcourt and Yola 55
Figure 4.6 Rain rate for Ayingba and Akure 56
Figure 4.7 Fade depth of all location 55
Figure 4.8 Attenuation at 0.01% exceedance for all locations 64
Figure 4.9 The effective path length for attenuations at 0.01% 67
Figure 4.10 Attenuation at C-band for horizontal polarization for all locations 68
Figure4.11 Attenuation at Ku-band for vertical polarization for all locations 69
Figure 4.12 Attenuation at Ka band for circular polarization for all locations 70
Figure4.13 Fade duration grouping by attenuation levels in (a) Port Harcourt(b) Lagos 71
Figure 4.14 Fade duration grouping by attenuation levels in (a) Mowe (b) Minna 72
Figure 4.15 Fade duration grouping by attenuation levels in (a) Akure (b) Nsukka 73
Figure 4.16 Fade duration grouping by attenuation levels in (a) Yola (b) Ayingba 74
LIST OF TABLES
Table 1.1: Frequency bands 5
Table3.1: Site characteristics of locations used 36
Table3.2 The values of k and α found from ITU-RRecommendation p.838. 45
Table4.1 Geometrical Parameters 60
Table 4.2 Estimates of specific attenuation and fade depth for all polarizations 62
Table4.3 attenuations (in dB) for 0.01% of the time and the effective path lengths 65
Table 4.4 Number of events for which duration exceeds threshold in Lagos 75
Table 4.5 Number of events for which duration exceeds threshold in Port Harcourt 75
Table 4.6 Number of events for which duration exceeds threshold in Yola 75
Table 4.7 Number of events for which duration exceeds threshold in Mowe 76
Table 4.8 Number of events for which duration exceeds threshold in Minna 76
Table 4.9 Number of events for which duration exceeds threshold in Akure 76
Table 4.10 Number of events for which duration exceeds threshold in Ayingba 77
Table 4.11 Number of events for which duration exceeds threshold in Nsukka 77
LIST OF ABBREVIATIONS
SATCOM Satellite Communications
DSD Rain Drop Size Distribution
UPC Uplink Power Control
ITU International Telecommunication Union
ACM Adaptive Code Modulation
VSAT Very Small Aperture Terminal
FMT Fade Mitigation Technique
SW South West
SS South South
SE South East
MB Middle Belt
NE North East
NASDRA National Space Research and Development Agency
TRODAN Tropospheric Data Acquisition Network
LOS Line-of-sight
CDs Cumulative distributions
ITCZ Inter Tropical Convergence Zone
CHAPTER ONE
INTRODUCTION
1.0 BACKGROUND TO THE STUDY
Telecommunications transmission facilities are the physical means of communicating large amounts of information over distance. Without exception, communication signals (speech, images, video, or computer data) are electromagnetic waves traveling along transmission lines such as 2-wire line, coaxial line, optical fiber and microwave link. For a given route, the type of transmission line selected dependson the topography, the amount of information to transmit, and the cost(Garlington,2006).
The presence of various forms of precipitation such as rain, snow, cloud and fog in a radio wave or microwave path is always capable of producing major impairment to terrestrial communications. Hydrometeors can introduce significant attenuation and depolarization, through their ability to absorb and scatter radio waves. (Shoewu and Edeko, 2011).
Consumer diversity, demands for bandwidth, and service convergence have led to a tremendous growth in communication systems. These have resulted in congestion at lower frequency bands, and consequently increased the need for higher frequency band usage. At these frequencies, however, the presence of rain causes degradation of signals, especially above 10 GHz. The many advantages of telecommunications systems operating at higher frequencies include: large bandwidth, increased frequency reuse, small device size and wide range of spectrum availability.
The major obstacle to these frequency ranges is rain ( Malinga, Owolawi and Afullo, 2013).
The impacts of rain rate along the satellite path in regions where mixed climate conditions
(tropical, sub-tropical and temperate) are common demand special attention with respect torain attenuation modeling. Electromagnetic waves passing through raindrops at any of thesebands will be absorbed, scattered, or passed through the medium. This scattering and absorption processes are termed rain attenuation.
Besides attenuation, rainfade is another major factor affecting theperformance of microwave links. Rain fade is thedynamic fluctuation of received signals due toinhomogeneities of the signal path, ranging froma few seconds to a few minutes. Rain fadeprovides additional information onunderstanding the characteristics of rain-induceddegradations(Islam, Rahman, Rahim, Al- tabatabaie and Abdulrahman, 2009).
. The effect of rainfall isobserved to be more severe in tropical regions which arecharacterized by heavy rain intensity and the presence oflarge rain drops (Ojo et al., 2008a; Moupfouma andMartin, 1995).
Commercial ground-space communications orSatellite Communications (SATCOM) havetraditionally been operating in the C band(4GHz to 8GHz), the first allocated frequencyband that is used predominantly for thereception of satellite television programmesin the early days of satellite communication. The C band is generally characterized by a largeconsumer antenna dish size of at least 2.4mand can offer wide coverage with highavailability due to its resilience in the presenceof heavy rain.
However, the C band occupancy for SATCOMwas soon congested due to the rapidemergence and deployment of terrestrialservices such as WiMax, terrestrial microwavenetworks as well as the sharing of limited Cfrequency spectrum with radar systems. Infact, there have already been cases of terrestrialinterference to satellite services in countrieslike Australia, Hong Kong and
Indonesia(Hartshorn, 2007).
At the same time, there is a growing global demandfor mobile, broadband (i.e. high data rate)applications via SATCOM such as airborne ormaritime internet broadband connectivity andland-based on-the-move applications.This string of events has prompted the needto explore the use of higher frequency rangessuch as Ku (11 GHz – 18GHz) and Ka (26.5GHz – 40GHz).
Industry analysis hasshown that Ku band transponders grew by20% (624 transponders) as
compared to the9% growth rate (260 transponders) for C bandtransponders between the years
2000 to 2003and this trend is expected to continue(Futron Corporation, 2003).
The main drawback in using these higher Kuand Ka frequencies over C band is thedegradation of the communication channelsdue to severe atmospheric and environmentalfade impact, particularly rainfall. Rain fade is considered a dominantimpairment for frequencies above 10GHz as itmay limit the availability of the link.
The propagation characteristics of the atmosphere that most strongly influence the fixed satellite service (FSS) systems are associated with rain. Rain on a satellite radio path causes fading, or
"rain attenuation." The attenuation in dB is roughly proportional to the square of the operating frequency, and rain that will cause a 2 dB fade on a 6 GHz uplink will wipe out a 30 GHz channel with about 50 dB of attenuation.
Rain also depolarizes satellite signals, converting energy from one polarization to another, and causes interference between channels that depend on orthogonal polarization for frequency re- use. At C- and K-band rain depolarization must be taken into account in dual-polarized systems.
1.3 FREQUENCY BANDS
Band Frequency range designation Users Radio communications, marine and mobile radio HF 0.003 to 0.03 GHz telephony FM, television broadcasts and line-of-sight ground-to-aircraft and aircraft-to-aircraft VHF 0.03 to 0.3 GHz communications, land mobile and maritime mobile communications, amateur radio, weather radio Television broadcasts, microwave oven, microwave devices/communications, radio astronomy, mobile phones, wireless LAN, UHF 0.3 to 1 GHz Bluetooth, ZigBee, GPS and two-way radios such as land mobile, FRS and GMRS radios, amateur radio Long wave: Global Positioning System (GPS) L 1 to 2 GHz carriers and also satellite mobile phones, such as Iridium S 2 to 4 GHz Short wave: Weather radar, surface ship radar,
and some communications satellites, especially those of NASA for communication with ISS and Space Shuttle Primarily used for satellite communications, for full-time satellite TV networks or raw satellite C 4 to 8 GHz feeds. Commonly used in areas that are subject to tropical rainfall. X 8 to 12 GHz Primarily used by the military. Used for satellite communications. In Europe, Ku- Ku 12 to 18 GHz band downlink is used from 10.7 GHz to 12.75 GHz for direct broadcast satellite services, Providing communications at sea, land and air; K 18 to 27 GHz World Space satellite radio, also used for broadcast satellite. Communications satellites and high-resolution, Ka 27 to 40 GHz close-range targeting radars on military aircraft.
Table 1.1: Frequency bands used for radio communication
1.1 SIGNAL FADING
The most troublesome and frustrating problem in receiving radio signals is variations in signal strength, most commonly known as fading. The fading may vary with time, geographical position or radio frequency, and is often modeled as a random process. Fadingmay either be due to multipath propagation, referred to as multipath induced fading, or due to shadowing from obstacles affecting the wave propagation, sometimes referred to as shadow fading.
There are several conditions that can produce fading. When a radio wave is refracted by the ionosphere or reflected from the Earth's surface, random changes in the polarization of the wave may occur. Vertically and horizontally mounted receiving antennas are designed to receive
vertically and horizontally polarized waves, respectively. Therefore, changes in polarization cause changes in the received signal level because of the inability of the antenna to receive polarization changes. Fading also results from absorption of the radioactive frequency energy in the ionosphere(Tekle and Ayele, 2011).
Rain fade is usually estimated experimentally and also can be calculated theoretically using scattering theory of rain drops. Rain drop size distribution (DSD) is an important consideration for studying rain fade characteristics. Various mathematical forms such as Gamma function, lognormal or exponential forms are usually used to model the DSD. Mie or Rayleigh scattering theory with point matching or t-matrix approach is used to calculate the scattering cross section, and specific rain attenuation. Since rain is a non-homogeneous process in both time and space, specific attenuationvaries with location, time and rain type
(https://en.wikipedia.org/wiki/Rain_fade).
.
Total rain attenuation is also dependent upon the spatial structure of rain field. Horizontal and vertical extensions of rain again vary for different rain types and location. Limit of the vertical rain region is usually assumed to coincide with 00 isotherm and called rain height. Melting layer height is also used as the limits of rain region and can be estimated from the bright band signature of radar reflectivity. The horizontal rain structure is assumed to have a cellular form, called rain cell. Rain cell sizes can vary from a few hundred meters to several kilometers and are dependent upon the rain type and location. Existence of very small size rain cells are recently observed in tropical rain (https://en.wikipedia.org/wiki/Rain_fade).
Possible ways to overcome the effects of rain fade are site diversity, uplink power control, variable rate encoding, receiving antennas larger (i.e. higher gain) than the required size for normal weather conditions.
1.1.1 CAUSES OF RAIN FADE
Any satellite communications system network operator using a Ku-Band system (12/14 GHz or higher frequencies) will face the effects of rain fade at some time. But to understand why this weakening occurs with higher frequencies, knowledge of the causes of rain fade is important.
Two of the most common causes are as follows.
1. ABSORPTION
Part or all of the energy generated when a radio wave strikes a rain droplet is converted to
heat energy and absorbed by the droplet.
2. SCATTERING
A non-uniform transmission medium (the raindrops in the atmosphere) causes energy to be
dispersed from its initial travel direction. Scattering can be caused by either refraction or
diffraction:
i. Refraction
Refraction is the change in direction of propagation of a wave due to a change in its transmission medium (Wikipedia). This occurs when signal travels through one medium to another especially
when both media have different refractive indices. In other words, the refractive index of the water droplet encountered by radio wave causes it to be refracted. The refractive index of water is dependent on both temperature and frequency.
ii. Diffraction
Diffraction is a term used to describe the phenomenon of electromagnetic waves bending around obstacles (Wikipedia). When a radio wave encountered a water droplet, the travel direction of the radio wave changes as it propagates around the water droplet (obstacle) in its path
These different reactions ultimately have the same effect they cause any satellite system to lose some of its normal signal level. Satellite signals do not get lost every time it rains, though. Rain outagewill only occur during the heaviest rains (convective and straitform are the most predominant types) withonly a small portion of the transmission path experiencing attenuation.
In fact, of a typical satellitetransmission path measuring 35888.371 km, less than .02% will be affected by rain fade.
Figure 1.0: Features characterizing the dynamics of fade events
1.1.2 FADE DEPTH
Fading is usually expressed in terms of fade depth. This is the difference between the maximum and minimum signal strengths over a certain interval oftime, usually over a very small interval.
The atmospheric irregularities along the radio-rays pathusually affect the velocity of propagated signal and consequently fading occurs. (Shoewu and Edeko, 2011).
.
1.1.3 FADE DURATION
Fade duration is defined as the time interval between two crossings above the same attenuation threshold. It is an important parameter to be taken into account in system design for several reasons:
1. System outage and unavailability: Fade duration statistics provides information on
the number of outages and the probability of the system being unavailable for a time
period of a given duration.
2. Fade mitigation techniques: Depending on the link margin, fade duration statistics
provide statistical information on the time durations the system stays in a
compensation configuration.
3. Coding and modulation: Fade duration is a key element in the process of choosing
forward error correction codes and best modulation schemes. The propagation channel
does not produce independent errors but blocks of errors. Fade duration impacts
directly on the choice of the coding scheme.
Of particular interest in the context of availability criteria is the distinction between fades of shorter and longer duration than 10 s. Knowledge of the distribution of fade duration as a function of fade depth is also a prerequisite for the application of risk concepts in the provision of telecommunication services (Michael,2008).
Fade duration distributions of rain attenuation are often modeled as the sum of two functions, one taking account of short durations and the second accounting for long durations. The short and long durations are usually assumed to be caused by scintillation and rain effects, respectively.
1.1.4 INTER-FADE DURATION
The inter-fade duration or non-fade duration (NFD) is defined as the time interval between two crossings below the same attenuation threshold. It is the complement of the fade duration; it is useful to characterize the time interval between two fades. Once the level of the received signal has just crossed back over the margin threshold after an outage event, it is essential to know statistically the duration before the occurrence of another fade event which may result in system outage. The inter-fade duration can be classified into short, intermediate and inter-event intervals. The short inter-fade interval segment accounts for tropospheric scintillation, and any fast amplitude variations resulting from rapid changes in rain dynamics. This interval ranges approximately from 1 to 10s. The intermediate range results from rain dynamicssuch as rain cell translation, and life cycle variations of rain cells. The range of the intermediate interval is approximately from 10s to several hours. The inter-event intervals are measured in days and represent the distribution of return periods of rain events.
1.1.5 FADE THRESHOLD
In Figure 1.0,the fade threshold as shown is the fading of signal amplitude as it crosses a certain threshold, this is the minimum level at which a signal can be received.
1.1.6 FADE SLOPE
Fade slope describes the rate of change of rain attenuation, A typical fade countermeasure system is open-loop uplink power control (ULPC), in which the attenuation on a uplink is estimated from the attenuation measured on the downlink and compensated by varying uplink power. Information regarding on fade slope is therefore important for determining the required tracking speed of the ULPC.
Fade slope depends on the attenuation level and the rain rate. This implies that the fadeslope will depend on the drop size distribution and therefore on the type of rain (convective or stratiform rain).
Another parameter of influence is the horizontal wind velocity perpendicular to the path which determines the speed at which the horizontal rain profile passes across the propagation path. The fade slope is likely to decrease with increasing path length. Thisis because a certain attenuation level on a longer path is more likely to be caused by widespread rain, or by several rain cells integrated over a longer distance, while the same attenuation on a shorter path is more likely to be due to a single, more intense rain cell (Michael Cheffena, 2008).
1.2 STATEMENT OF PROBLEM
Fading due to rainfall can severely degrade the radio wave propagationat centimetre or
millimeter wavelengths. It restricts the path length of radio communication systems and
limits the use of higher frequencies for line-of-sight microwave links as well as satellite
communications. Signal fading pose great problems to communication as the frequency
of occurrence of heavy rain increases. In this light, for Nigeria that is in the tropical
region, the knowledge of the rain fade at the frequency of operation is extremely required
for the design of a reliable terrestrial and earth space communication link at any location
of interest in order to avoid problems associated
This project will provide information on the amount of fade depths, outages and system
availability and fade duration statistics conditional distributions of the number of fades
exceeding certain durations, given that specified fade threshold has been exceeded.
1.3 OBJECTIVE
This research aims atanalyzing the effects of rain fade in Nigeria and the counter
measures that can be employed to mitigate them. This is achieved through the estimation
of rain attenuation on terrestrial point-to-point microwave link and using adopting ITU-R
models for the evaluation of rain fades
1.4 SCOPE OF PROJECT
This project involve analysis of rain fades recorded on the Tropospheric Data Acquisition
Network (TRODAN) stations that are located in Redeemer’s University (Mowe), Lagos,
Port Harcourt, Minna, Enugu,Akure,Yola, and Ayingba.
Figure 1.1: Map of Nigeria showing study areas
CHAPTER TWO
2.0 LITERATURE REVIEW
Several efforts have been made by many researchers to estimate the level of degradation of terrestrial and satellite signals in Nigeria based on ITU models. However, most of the investigations are based on the cumulative distribution of rain –induced attenuation while very limited investigations have been focused on fade dynamics of rain attenuation statistics.
Ojo and Ajewole (2011) workedon, Dimensional statistics of rainfall signature and fade duration for microwave propagation in Nigeria. They were able to establish that at higher time percentage up to 0.2% for higher frequency of 30 GHz, the values of attenuation obtained is higherthan the values of rain rate that produces it.
Among other researchers who have also reported their findings are Lee, Koh, Yuen and
Michelle, Lee et al (2013). Their findings have been very useful for communication planning and system design as they suggested various ways of overcoming the rain fade in their research on understanding rain dynamics and feasible countermeasures against rain fading in Singapore.
They got conflicting results which potentially suggest that the available rain models might not provide anaccurate prediction of rain attenuation for tropical areas; thereby suggesting fade mitigation techniques. The performance of the site diversity technique, carried out in Singapore, was evaluated with Ku band signals from INTELSAT. These signals were monitored at two earth stations, namely NTU and Bukit Timah (BKT) which are separated by 12.3km (Isaiah and
Choo, 2000). The cumulative distribution of attenuationmeasured at NTU and BKT was monitored, it can be seen that when the site diversity technique is employed, the overall level of attenuation will decrease. For instance, the exceedance percentage is reduced from 0.25% to
0.035% for a typical link margin of 7dB which translates to a decrease of 18.8 hours insignal outage in a year.
Omotosho, (2008) carried out his research work on the study investigations on the effect of propagation impairments such as rain, cloud, gases and tropospheric scintillation on fixed satellite communication link on earth-space path for frequencies between 10 and 50 GHz at Ku,
Ka and V bands for 37 locations in Nigeria. Elevation angles of 500 and 550 of Nigeria
Communication Satellite, (NigComsat-1) were used in the computation of the propagation impairments for the 37 locations. The International Telecommunication Union Radiowave
Propagation models (ITU-RP) were used in the investigation of the propagation impairments. He concluded that Tropospheric scintillation is very high in the SS region and combined impairments due to multiple sources of simultaneously occurring atmospheric attenuation is highly severe in SE .Overall, Sokoto and Katsina appear as good locations to site fixed satellite earth stations (operating at Ku band and above).
Khandaker and Mohammad (2014) studiedthe performance analysis of rain fades on microwave earth-to-satellite links in Bangladesh, one-minute integration time rain intensity data were derived from last thirteen years annual rainfall statistics measured at 34 meteorological stations in Bangladesh. The converted rain intensity data are used to estimate rain fades at C, Ku and Ka- bands, but did not treat other areas of fade dynamics like the fade duration and the fade slope.
However, Yen-Wu Chen, Vice President, Asian Operations, Kratos ISI also used the approach of
Adaptive Code Modulation (ACM) where the modulation/coding is modified to operate in a lower power environment. But ACM can help maintain service during some rain events but cannot overcome all, especially intense events. Additionally, ACM will impact achievable data
rates during the rain fade event and is good only for services that do not need a minimum data rate services requirement (e.g., many packet-based data services).
More recent research efforts have focused on determining attenuation statistics at higher percentages of time, say 10 percent to 0.1 percent, since these correspond to the reliabilities and the 1 to 3 dB fade margins that very small aperture terminal (VSAT) systems and other low margin the services provide.
Rainfall is characterized by space and time variable structure constituted by cells of various dimensions that move horizontally with speed depending on the tropospheric winds and the height of the clouds. Radar measurements have shown that typical dimensions of strong rain rate cells range from 2 to 5 km (Adimula and Ajayi, 1996). The height of the rain cell (rain height) is an important parameter in the calculation of slant path attenuation. It is generally considered that the rain system reaches a maximum height equal to the 00C isotherm, above this precipitation it is assumed to have the form of ice, snow, or melting snow (ITU-RP, 839, 2001).
However, E. O. Ogunti (2016) studied the effect of rain fade in satellite communication in his paper: Making Sense from knowledge Management Concept where he Suggested the use of questionnaire for collection of data, to be carried out by meeting with consumers of this the particular service to ask them the questions that would be needed to proffer solution to this rain fade problem
2.1 Raindrop size and shape
In the millimeter-wave range of the radio spectrum both the shape and the size of the raindrop are important. In addition, for a particular raindrop, the drop shape will depend on its size and the rate at which it is falling. In order to model the effects of rain attenuation and scattering of radio- waves, rainfall is usually characterized by drop-size distribution, N (D), which is defined as the number of raindrops falling per cubic meter, with drop diameters, D, in the range D to D+dD.
The drop-size distribution is a function of the rain rate, R, which is usually measured in mm/hr.
Other parameters include the fall velocity of the drops and, the time of the year (Bonn, 1994).
Model predictions of attenuation due to rain had been standardized and reported in ITU-R P.838,
2005.
2.2 RAIN RATE PREDICTION MODELS.
Rain rate models are used to predict the point rainfall-rate cumulative distribution of any location. Several of such models exist. However, some of them have one discrepancy or the other, such as the number of stations and data available and not all the stations satisfy the one- minute integration time requirement (Crane, 1982); some require a relatively high density of short integration time (Stutzmann and Dishman, 1982).
Rice and Holmberg (1973) developed a model for obtaining rain rate values for use in fading calculations. The model requires parameters like the highest monthly rainfall accumulation observed in a set of 30-year period, number of thunderstorm days expected in an average year and the average annual accumulation. The limitation is that the thunderstorm ratio is not readily
available from local weather agencies. However, this model overestimates rain rates in the high availability range of 0.01% and underestimates rain rates in the low availability range of 0.1% and 1% (Ryde, 1946).
Chebil and Rahman (1999) proposed a model which is used to convert the rain amount data of any location to its equivalent rain rate data irrespective of the integration time of the available rain data. It uses a long-time mean annual accumulation, M, of rain collected for the location under study and it is expressed by (Chebil and Rahman 1999) as:
ᵦ R0.01 = αM ………………………………………………………………………… (1)
Where α and β are regression coefficients and are α= 12.2903; and β= 0.2973,
M is the total rain fall measured for a year and rain rate R0.01is measured in mm/h.
Recent analysis suggests that the rain rate distribution is better described by a model which approximates a log-normal distribution at low rate and a gamma distribution at high rate. The model was developed by Moupfouma and Martin (1995). This model known as the Moupfouma and Martin’s rain rate model is good for both tropical and temperate regions. It is expressed by
(Moupfouma, 2009) as:
b (μ (R0.01 – r))………………………………. (2)
where P is the probability of a rain event at 0.01%of the time, r (mm/h) represents the rain rate exceeded for a fraction of the time, R0.01 (mm/h) is the rain intensity exceeded during 0.01% of time in an average year and b is approximated by the following expression (Moupfouma, 2009):
…………………………………………………… (3)
The slope of the rain cumulative distribution is governed by the parameter µ, which depends on the local climate conditions and geographic features. For tropical and subtropical regions,
ϒ ……………………………………………… (4)
λ and ϒ are positive constants and are given as λ = 1.066, ϒ =0.214 and R0.01 is the rain rate exceeded for 0.01% of time and is obtained using Chebil and Rahman (1999) model.
Thus the refined Moupfouma model can be used to determine the one-minute rain rate cumulative distribution from the long term mean annual rainfall rate.
The third known rain rate prediction model is the ITU recommended model, which depends on the Salonen-Baptista model (Salonen and Poiares-Baptiste, 1997). It is used to calculate the rain rate at a given location based on the geographic coordinates. ITU
Recommendations P1144-3 and P837-4 are combined to obtain the rain rate (mm/h). The
ITU-P 837-4 model requires as input the following parameters:
Ms defined as the mean annual straitform rainfallamount (mm)
Mcdefined as the mean annualconvective rainfall amount (mm)and
Pr6 defined as the probability of a given rainy period (%).
ITU has the parameters given above mapped out all overthe world using 15 years of re-analysis products of theEuropean center for medium range weather forecasts(ECMWF ERA 15 data set).
To obtain rain rate using ITUmodel, the following steps are adopted:
STEP 1
The variables Pr6, Ms and Mc are extracted for the fourpoints closest in latitude (Lat.) and longitude (Lon.) to thegeographical coordinates of the location under study.
STEP 2
From the values of Pr6, Ms and Mc at the four grid points,the values Pr6 (Lat, Lon), Mc (Lat.,
Lon) and Ms (Lat., Lon)are obtained by performing bi-linear interpolation
Given values at four surrounding grid point: I(R, C); I(R,C+1); I(R+1, C) and I(R+1, C+1), I(r, c) can be obtainedwhen r is a fractional row number and c is a fractionalcolumn number using bi-polar interpolation as ((ITU- R,2009)):
I (r,c) I(R,C)[(R 1 – r)(C 1 – c)] I(R 1,C) [(r – R)(C 1 – c)] I(R,C 1)[(R 1 – r)(c –
C)] I(R 1,C 1) [(r – R)(c – C)] (ITU-R P.1144) where R, C, r, c are values at four surrounding gridpoints of the location under study.
STEP 3
The percentages of probability (P0) of rain in an average year are obtained from (ITU, 2001)
0.0079 (Ms (Lat,Lon)/Pr6 (Lat,Lon) P0(Lat,Lon) Pr6(Lat,Lon)1e …………………………… (5)
If Pr6 is equal to zero, the percentage probability of rain in an average year and the rainfall rate exceeded for any percentage of an average year are equal to zero. In this case, the following steps are unnecessary.
STEP 4
Derive the rainfall rate, Rp, exceeded for p% of the average year, where pP0, from:
B B 2 4AC R (Lat, Lon) mm/h ……………………………………….. (6) p 2A where:
A ab………………………………………………………………………….(6a)
B ac ln(p /P0(Lat,Lon)) ……………………………………………………. (6b)
C ln(p /P0(Lat,Lon)) ………………………………………………………… (6c) and
a 1.09 ……………………………………………………………………….. (6d)
(M (Lat,Lon) M (Lat,Lon)) b c s …………………………………………. (6d) 21797P0
c 26.02b …………………………………………………………………….. (6e)
2.3 RAIN FADE MITIGATION TECHNIQUES
The occurrence of rain outage does notnecessarily suggest that there will be acomplete total disruption to thecommunications link. To overcome the dynamics of outages, fade mitigation technique (FMT) is undeniably acritical element in the design of SATCOMnetworks as the
effects of fading can becombated by exploiting the correct mitigationmeasure(s) to improve the communicationsavailability and reliability of satellite links. Inother words, the incorporation of
FMTthroughout the chain from the user terminal,the spaceborne payload to the overall networkdesign, can potentially offer a reduction inoutage time
1. Uplink Power Control (UPC)
The simplest way to compensate for the rainfade effect is to increase the transmission
powerat the terminal end. This method is known asuplink power control. However,
constanttransmission of high power may result ininterference among users during clear
skyconditions. The underlying technologytherefore resides in the dynamism of the
systemthat is capable of adjusting the power, inresponse to the fading variations, to
therequired signal level essential for higherfrequency operations. Three types of uplink
power control algorithms can be executed tomaintain carrier power or signal quality
duringrainy periods.
i. Open loop power control
The satellitegenerates a beacon signal to the receivingground station which is used to
ascertainthe level of downlink attenuation. Thepower controller at the ground station
thenestimates the uplink fade required byapplying the frequency scaling ratios
forcloud, gaseous and tropospheric scintillationattenuation.
ii. Closed loop power control
Similar to theopen loop power control architecture, theground station utilizes
aloopbackcommunication signal instead. Itstransmitted beacon signal is analyzed
toestimate and counteract for the rainattenuation i.e. the received signal
comprisesboth uplink and downlink rain effectdegradation.
iii. Feedback loop power control
A centralstation monitors the signal levels of all thereceived carriers and analyses the
poweradjustment needed for the affected carriers.
This control information and command aresubsequently routed to the
transmittingground station for the corrective measure(s)to be effected.
Though uplink power control is affordable andsimple, it provides marginal benefit as
it cannotcontinuously amplify its margin. Most amplifiersexhibit a non-linear
behaviour and the outputpower will be limited despite an increasein power (Lee Yuen
Sin, and Michelle Ho Xiu Mei, 2013).
2. Site Diversity
While it is useful to know what kind of a fade margin a satellite link must provide to achieve a stated reliability, for most Ka-band terminals of any size and for some Ku-band terminals, the margin may be too large to implement in practice. In this case, systems may be designed to overcome rain fading by using site diversity, Uplink Power Control(UPC), variable rate encoding, or combinations of these techniques.
A site diversity system uses two or more earth stations in a redundancy configuration on the usually valid assumption that attenuation will not be as great at two stations simultaneously as it is at either one of them. Diversity systems have not generally been found to be cost effective.
In systems using UPC and/or variable rate encoding, the quality of the received signal is monitored and, during a fade, the transmitting station either increases power to compensate for the fade or changes the encoding rate to maintain an acceptable bit error rate on the attenuated signal. For satellite systems, this means that the satellite must have reserve capacity (power or bandwidth or both) which can be dynamically allocated to those links which are experiencing fades, and that the earth stations must be able to sense fades and either compensate for them themselves or request the satellite or the earth station at the other end of the link to do so. The round-trip delays involved in geostationary orbit satellite paths complicate this last part of the process (Lee, and Michelle, 2013).
3. Another approach is Adaptive Code Modulation (ACM)
Adaptive Coding and Modulation or Link adaptation is a term used in wireless
communications to denote the matching of the modulation, coding and other signal and
protocol parameters to the conditions on the radio link (e.g. the path loss, the interference due
to signals coming from other transmitters, the sensitivity of the receiver, the available
transmitter power margin, etc.)
The goal of Adaptive Modulation and Coding is to improve the operational efficiency of
Microwave links by increasing network capacity over the existing infrastructure – while
reducing sensitivity to environmental interferences(Wireless Excellence Limited, 2016).
ACM can help maintain service during some rain events but cannot overcome all, especially
intense events. Additionally, ACM will impact achievable data rates during the rain fade
event and is good only for services that do not need a minimum data rate services
requirement (e.g. many packet-based data services). Additionally, there can be a lack of
interoperability among vendors and higher cost modems are usually required (Yen-Wu Chen
2015).
2.4 Fixed wireless link power budget
Once the availability objectives are specified for the designed radio path, the link parameters have to be considered in order to meet these objectives. Propagation related bit errors that occur on millimetre wave links are caused dominantly by poor detection when the received signal power falls under the receiver threshold due to the attenuation of incoming electromagnetic waves. This is why the link power budget has to be determined above all. The nominal power available at the input of the receiver can be obtained from the following formula:
Pr= Pt– Lt+ Gt– FSL + Gr– Lr………………………………………(7)
where Pt (dBm) is the power at the output of the transmitter, Pr (dBm) is the power at
theinput of the receiver, Gt and Gr (dB) are transmitting and receiving antenna gains, Lt and
Lr(dB) are additional losses (branching, feeder,…) in the transmitter and receiver and FSL
(dB)is the free space loss which is dependent on the path length d (m) and wavelength λ (m):
FSL= 20 log(4πd/λ)………………………………………………….. (8)
Normally, the nominal received power Pr is much higher than the receiver threshold
Prth(dBm) which is an important parameter of the receiver that depends on the
modulationformat and on the receiver noise figure. Prthis usually defined as the power at the
input ofthe receiver that will result in a certain threshold value of bit error ratio BER, typically
10-6or
10-3. It follows that the fade margin F (dB):
F= Pr- Prth)…………………………………………………………… (9)
The fade margin is the difference between the received signal strength and the radio receiver sensitivity. It means when a link is deployed to have a receive signal strength that is sufficiently above the radio Receiver Sensitivity in order to survive signal fading due to a variety of factors.
These factors might include slight misalignment of the antennas, losses due to fog and rain, etc
(Wikipedia). It is also a design allowance that provides for sufficient system gain or sensitivity to accommodate expected fading, for the purpose of ensuring that the required quality of service is maintained. It determines the maximum attenuation of the received signal that maintains a
BER lower thanthis threshold. Given the required maximum of unavailability time percentage p
(%)according to the objectives described in the previous section, rain attenuation Ap
(dB)exceeded p % of time is derived from rain attenuation statistics. In order to meet theavailability performance objectives, link parameters (antenna gain, link length has to beadjusted so that the following condition for the fade margin is satisfied(Vaclav and Martin,
2007).
F ≥ Ap.)………………………………………………………. (10)
2.5 Rain attenuation models
Various methods were developed for the calculations of cummulative distributions (CDs) of attenuation due to rain fromrain intensity measurements (COST 235, 1996; ITU-R, 2008). The
ITU-R recommendation(Rec. ITU-R P. 530-12, 2008)) uses an effective path length to consider the time-spacevariability of rain intensity along the terrestrial path. Rain attenuation exceeded at
0.01% ofthe time of year is calculated from the average 1-minute rain intensity exceeded at the sametime percentage. The obtained value is scaled by the empirical formula to other percentagesof time between 1% and 0.001%(Vaclav and Martin, 2007).
2.5.1 Slant path rain attenuation models
The ITU-R P.618-9 and P.839-3 model, which is the most widely accepted international method and benchmark for comparative studies of path attenuation. This model is semi- empirical and often employs the local climatic parameters at a desired probability of exceedance (Parth and
Rutvij, 2016).
Rain attenuation can either be obtained directly from microwave link measurements, orestimated from the rain rate and rain drop-size distribution data. Ajayi, et al (1990) provided a methodological approach for estimating rain-induced attenuation from availableprecipitation data
(or, rain statistics), using method of moment of regression to estimate thenumber of drop size, and compared the results with log-normal distribution.
However, majority of rain attenuation estimation in tropical regions are now based on
Moupfouma model, which is good for both temperate and tropical climates as mentionedabove.
ITU-Recommendations (as will be seen in the methodology) have provided step-by- stepmethodologies for calculating the rain attenuation over the Earth-satellite radio path
(Abdulrahman,Islam, Rahman, and Rahim, 2014).
2.6 ITU-R PREDICTION OF RAINFALL OVERTROPICAL REGION
In tropical countries communication for higher frequencies aredistributed due to high rain rate.
Rain strongly attenuates theradio waves above 10 GHz which is a main impediment tosatellite
link performance.
Bhattacharya et al. studied the variations of rainfallpattern which were helpful for predicting
propagation conditions.Distribution of rain attenuation at 11.7 GHz and 13.4 GHz for 56
0elevation angle over Delhi showed that CCIR modelunderestimates the attenuation over India.
It is also found by Moupfouma that excess attenuation due to rainfall above acertain threshold
frequencies limits the LOS links and he in hisresearch work mentioned that there is a need to
improve the rainattenuation prediction model for terrestrial microwave links onwhich basis he
provided many new steps and results.
Bryantet al. worked on the rain cell diameters. His study revealsthat deterioration of a
communication link at 13 GHz inmonsoon month revealed that the communication link did
notserve the purpose 5% of time. Full Communication link can beachieved during monsoon
months if an extra gain of 12-15 dB isprovided to the transmitting system. The attenuation is
found tobe 0.5 dB/km at 22.235 GHz during monsoon months.According to ITU-R (CCIR)
rain climatic zones havebeen designed following the characteristics of precipitation of path
propagation modeling. Prediction of path attenuationby ITU-R underestimates the radio
metrically derived cumulativedistributions (CDs) of path attenuation, in general. Also the
ITURprocedure may not match well with the rainfall ratecharacteristics. The model only
predicts rain induced attenuation.
It also shows total attenuation statistical for measured annual,worst month and predicted (ITU-R method) cumulative statistics.It needs knowledge of the rain rate exceeded 0.01% of the timeas measured using a gauge with one minute integration of time.These factors contribute to overestimation of attenuation by ITURmodel. Other factors may also be responsible for the differencebetween the measured and predicted attenuations(Mukesh,Rohiti, Deepak and
Sumeet, 2015).
2.7 RAIN HEIGHT AND NOISE TEMPERATURE
Estimation of rain attenuation along the slant-path of a satellite link requires an understanding of rain height. The method, adopted by ITU-R, assumes the rain structure to be uniform from the
0 ground level to the 0 C isotherm height, hR, simply termed the effective rain height. Often the empirical formula is used to estimate the value of hRdue to a scarcity of measured data. Most of the referenced rain height experiments were done in Europe and Asia, and very little data is available in Africa except in West Africa. As a result, the current work uses the latest ITU
Recommendation P.839-3. Though the model is less accurate, it is widely employed to calculatethe average rain height. The mean rain height above mean sea level is expressed as:
hR= h0 + 0.36 km …………………………………………………… (11)
0 where h0 is the average annual 0 C isotherm height. If the h0 is not available from local data,a global contour map is used.
2.8 FADE DEPTH PREDICTION
ITU-R 530-16 provides techniques forestimating the percentage of time that a fade depth is exceeded inthe average worst month. It further recommends prediction methodsbased on specific climatic and topographical conditions. Region-basedtechniques for deep fading predictions have been available for a numberof countries for several years. They include the Barnet-Vigants modelfor United States and the Morita model for Japan, among others.
These techniques for predicting the percentage of time that acertain fade depth is exceeded are a function of frequency, path lengthand geoclimatic factor, with the ITU-R method having an additionalvariable of path inclination. The ITU-R suggests that region-basedmethods are likely to be more accurate than the ITU-R technique inthe estimation of the percentage of time that a fade depth exceeded in the worstmonth because of the effect of the geoclimatic factor. They furtherrecommended that in determining the geoclimatic factor, fading datain the region of interest should be used in the estimation.
2.9 FADE DURATION DISTRIBUTION MODELS
1. ITU-R model.
ITU-R model is able to calculate fade duration statistics including effect of gases, clouds, rain and scintillation for earth-satellite path links. The model presents long-term of fade duration
follows a lognormal distribution and short-term of fade duration follows a power-law distribution. Calculation of P(d>D | a>A) of duration dlonger than Dgiven that attenuation ais greater than Aas (1-2).
– For 1 D Dt P(d D|a A) D …………………………………… (12)
ln(D) – ln(D ) Q 2 – For D Dt P(d D|a A) D t ln(D ) – ln(D ) Q t 2 ……………………… (13)
where: exponent γof the power-law distribution of the fraction of fading time due to fades of short duration and Dt, is boundary between short and long fade durations. Details of the ITU-R model are available in the methodology.
1 Timothy model.The model is developed by normalizing a lognormal distribution that
depends on the average fade duration . If fractions of fade events exceeding the normalised
fade duration, the fade duration distribution approximation is given by
where x= D/ is the normalized fade duration, is the average fade duration for a particular threshold, 1is the standard deviation of ln(D/ , erfcis the complementary of error function, and is the mean of
CHAPTER THREE
MATERIALS ANDMETHODOLOGY
3.0 EQUIPMENT
For this study, the data used were obtained from an Automatic Weather Stations Network
installed across Nigeria under the umbrella of Tropospheric Data Acquisition Network
(TRODAN).It is provided by National Space Research and Development Agency
(NASRDA), a Federal Government Agency that provides research facilities and services
for the atmospheric and Earth sciences community in Nigeria.
This equipment monitor the lower atmosphere which covers region from the surface of the Earth to an altitude of about 11 kmand carries out simultaneous measurements of meteorological and climatological variables, in real time, through telemetry technology, with five minutes update cycles.
It is noteworthy that the Automated Weather Stations are invaluable tools for researchers, environmentalist, academics, policy makers and students at all levels; helping them to forge ahead in their respective tasks.
3.1 INSTRUMENTATION
Figure 3.1 below shows the TRODAN equipment at the Redemption Camp. This station is representative of what can be found at other local stations.
Figure 3.1: The TRODAN station at the Redemption Camp, Mowe, Ogun state.
Variables being generated from each location include:
1. Air temperature (Degree C)
2. Relative humidity (%)
3. Precipitation (mm)
4. Atmospheric pressure (mbar)
5. Wind speed(m/s)
6. Direction (0N)
7. Solar radiation (W/m2)
8. Soil moisture (%)
9. Soil temperature(0C)
10. Rain rate (mm/min)
11. Other derived variables
Each station is built with sensors, solar power system for uninterruptible power supply,
measurement and control system and data logging system.
Each station basically, is software controlled. This has aided the precision, accuracy and
performance of the associated devices.
The system generally runs on the following softwares:
3.2 DATA
Datawere collected from eight (8) different TRODANstations in Nigeria. The locations
and their characteristics are as presented in Table 1 and Figure 3 respectively.
HEIGHT AVERAGE OBSERVATION STATIONS LATITUDE RAIN ELEVATION ABOVE SEA ANNUAL PERIOD (0N) RATE ANGLE (θ) LEVEL (mm) RAINFALL (R0.01) (MM/YEAR)
12 months (Jan. 2011- MOWE 6.8184 74.83 50.5 19.00 430.5 Dec. 2011) 12 months (Oct.2007- LAGOS 6.5200 110.7246 51.5 10.75 1626.2 Nov. 2008 12 months (Jan. 2009 PORT HARCOURT 4.8156 123.47 55.9 7.52 2346.1 – Dec. 2009) 2 years (Jan. 2008- NSUKKA 6.8429 106.51676 56.1 414.40 1427.15 Dec. 2009) 12 months (Jan. 2010 MINNA 9.5836 90.14530 54.2 273.3 814.328 – Dec. 2010)
AKURE 7.3106 107.77 50.4 367.03 1485.6 16 months (Jun. 2010 –Sept. 2010)
YOLA 9.0766 94.32.69 60.7 282.63 948.5 14 months (Nov.2009– Dec. 2010) AYINGBA 7.4934 70.1077 55.5 377.42 349.6 12 months (July 2010 –July 2011)
Table 3.1: Site characteristics of locations used
Figure 3.2: Map of Nigeria showing the locations where data were taken for this work
3.3 RAINFADE CALCULATIONS
Rain fades vary with frequency, location, polarization and rainfall rate. The depth of fade in
decibel (dB) can be calculated from:
LR = R DRAIN……………………………………………………………………………(16)
LRAIN is the rain loss in dB
R is the specific attenuation (dB/km)
DRAIN is the path length through the troposphere in km,
To calculate the rain attenuation we need to know:
• Latitude and longitude of the earth station to within a degree.
• Altitude of the station in mm.
• The frequency of operation
• The polarization of the signal.
• The required availability of the satellite circuit.
DETERMINING DRAIN
DRAIN is effectively the slant range of the portion of the signal that lies below the freezing
point (0 0C isotherm) in the atmosphere. The assumption is that all rain originates at this
level
Figure 3.3: A schematic diagram of earth-space showing the slant range of the portion of
the signal that lies below the freezing point (ITU-RP 618, 2003)
DRain can be calculated from simple trigonometry from the above diagram.
DRain = (hRain - hANTENNA) Sin e ……………………………………………………… (17)
1. RAIN HEIGHT AND NOISE TEMPERATURE
Estimation of rain attenuation along the slant-path of a satellite link requires an
understanding of rain height. The method, adopted by ITU-R Recommendation P.839,
assumes the rain structure to be uniform from the ground level to the 00C isotherm height,
hR, simply termed the effective rain height. Often the empirical formula is used to
estimate the value of hR due to a scarcity of measured data. Most of the referenced rain
height experiments were done in Europe and Asia, and very little data is available in
Africa except in West Africa (Malinga, Owolawi,2013). The model is used to calculate
the mean rain height above mean sea level and is expressed as:
hR = h0 + 0.36km ………………………………………….. (18)
2. RAIN ATTENUATION
Rain attenuationis estimated by integrating the specific attenuation along the earth-space path.
The specific rainattenuation is mathematically calculated by using empirical parameters such as the cumulativedistribution of one-minute rain rate at a givenprobability of exceedance. In this research work, estimated specific rain attenuation at different frequency bands is proposed with
Specificattention given to C, Ku and Ka bands using the ITU-R.
3. RAIN RATE MODEL USED.
The rain rate model used in this research work is Moupfouma model (opcit). Rain rate models are used to predict the point rainfall-rate cumulative distribution of any location.
Several studies have shown that the Moupfouma model with refined parameters can best describe the one minute rain rate distribution in tropical regions. Moupfoumafound that the one minute rain rate CD could be expressed as
-4 b P(R r) =10 R0.01 + 1 exp ( [R0.01 - r]) …………………………………… (19) r + 1
where r (mm/h) represents the rain rate exceeded for a fraction of the time, and b is approximated by the following expressions
b= r – R0.01 ln 1 + r R0.01 R0.01 …………………………………………… (20)
METHOD OF RAIN ATTENUATION PREDICTION
In order to calculate the rain fade depth in the selected locations, the specific attenuations must be determined.
1. ITU-R Rain Attenuation Model
In this section, a rain attenuation model is presented that has performed well for many regions and different rain types. This rain attenuation model is the ITU-R model, which is the most widely accepted international method and benchmark for comparative studies. This model is semi- empirical and often employs the local climatic parameters at a desired probability of exceedance.
The ITU-R 618-10gives summarized procedures for the computation of a satellite path rain attenuation. In order to compute the slant-path rain attenuation using point rainfall rate, the following parameters are required:
f: the frequency of operation in GHz,
µ: the elevation angle to the satellite, in degrees,
Á: the latitude of the ground station, in degrees N and S,
hs: the height of the ground station above sea level, in km,
Re: effective radius of the Earth (8 500 km),
R0:01: point rainfall rate for the location of interest for 0.01% of an average year
(mm/hr).
The geometry is illustrated in Figure 3
Figure 3.4: A schematic diagram of earth-space path giving the parameters to be input into the attenuation prediction process (ITU-RP 618, 2003).
Where
A: Frozen precipitation
B: Rain height
C: Liquid precipitation
D: Earth-space path
Step-by-step procedures for the computation of the rain attenuation along the slant-path of a satellite system are summarized as follows:
Step 1: Determine the rain height, hR, as given in table 1
Step 2: Determine the slant-path length and the horizontal projection.
The slant-path length Ls, expressed in km, is calculated from:
(hR hs) Ls km …………………………………………………………..(21) sin
For 5, the following formula is used:
2(h h ) L R s km ...... (22) s 1/ 2 2 2(hR hs) sin sin Re
Step 3: Calculate the horizontal projection, LG, of the slant-path length from:
LG Ls cos km ………………………………………………………….. (23)
Step 4: Determine the rain rate at 0.01% for the location of interest over an average year. In this work, Table 3 showed derived rain rate at one-minute integration time at 0.01% of exceedance from data collected from TRODAN stations.
ᵦ R0.01 = αM …………………………………………………………………………… (24)
Where α and β are regression coefficients and are defined as;
α = 12.2903; and β = 0.2973,
Step5:Obtain the specific attenuation,RR, using the frequency-dependent coefficients given in
Recommendation ITU-R P.838 and the rainfall rate, R0.01, determined from Step 4, by using:
R k (R0.01) dB/km ...... (25)
The values of K and α for both vertical and horizontal polarization at different frequencies which is found from ITU-R Recommendation P.838 is on table 4
Step 6: Calculate the horizontal reduction factor, r0.01, for 0.01% of the time:
1 r0.01 ...... (26) L 1 0.78 G R 0.38 1 e2LG f
Step 7: Calculate the vertical adjustment factor, v0.01, for 0.01% of the time:
–1 hR – hs tan degrees LG r0.01
L r For , L G 0.01 km R cos
(h – h ) Else, L R s km R sin
If | | 36, 36 – | | degrees
Else, 0 degrees
1 ν0.01 /(1 ) L 1 sin 31 1 – e– R R – 0.45 2 f
Step 8: The effective path length is:
LE LR 0.01 km …………………………………………………………… (27)
Step 9: The predicted attenuation exceeded for 0.01% of an average year is obtained from:
A0.01 R LE dB…………………………………………………………... (28)
Step 10: The estimated attenuation to be exceeded for other percentages of an average year, in the range 0.001% to 5%, is determined from the attenuation to be exceeded for 0.01% for an average year:
If p 1% or | | 36: 0
If p< 1% and | | < 36 and 25: –0.005(| | – 36)
Otherwise: –0.005(| | – 36) + 1.8 – 4.25 sin
–(0.655 0.0331n( p) – 0.0451n( A ) – (1 – p) sin ) p 0.01 Ap A0.01 dB ...... (29) 0.01
Where
= 0 if p ≥ 1% or /ᵠ/ ≥ 360 -0:005(/ᵠ/ -360)if p <1% and /ᵠ/ <360 and ≥250 -0:005(/ᵠ/ - 360) + 1.8 – 4.25 sin otherwise
This method provides an estimate of the long-term statistics of attenuation due to rain.
Table 3,2 : The values of K and α found from ITU-R Recommendation P.838.
3.4 FADE DURATION MODEL USED
The Fade duration prediction method used in this study is the model recommended by the
ITU-R p..1623-1, under the heading: prediction method for fade dynamics on Earth-
space path.
Fade duration can be described by two different cumulative distribution functions:
1 P(d > D|a >A), the probability of occurrence of fades of duration d longer than D (s),
given that the attenuation a is greater than A (dB). This probability can be estimated from
the ratio of the number of fades of duration longer than D to the total number of fades
observed, given that the threshold A is exceeded
2 F(d > D|a >A), the cumulative exceedance probability, or, equivalently, the total fraction
(between 0 and 1) of fade time due to fades of duration d longer than D (s), given that the
attenuation a is greater than A (dB). This probability can be estimated from the ratio of
the total fading time due to fades of duration longer than D given that the threshold A is
exceeded, to the total exceedance time of the threshold.
The model is expected to be valid for durations longer than 1 s.
The following parameters are required as input to the model:
f : frequency (GHz): 10-50 GHz
: elevation angle (degrees): 5-60°
A : attenuation threshold (dB).
The step-by-step calculation of the fade duration distribution is as follows:
Step 1: Calculate the mean duration D0 of the log-normal distribution of the fraction of fading time due to fades of long duration, given that the attenuation is greater than A, as:
–0.4 1.4 –0.39 D0 80 f A s ...... (30)
Step 2: Calculate the standard deviation of the lognormal distribution of the fraction of fading time due to fades of long duration as:
1.85 f –0.05 A–0.027 ...... (31)
Step 3: Calculate the exponent of the power-law distribution of the fraction of fading time due to fades of short duration as:
0.055 f 0.65 A–0.003 ...... (32)
Step 4: Calculate the boundary between short and long fade durations, Dt, as:
2 p1 p2 – 0.39 Dt D0 e s ...... (33) where:
p1 0.885 – 0.814...... (34)
2 p2 –1.05 2.23 – 1.61 ...... (35)
Step 5: Calculate the mean duration D2 of the log-normal distribution of the probability of occurrence of fading events of long duration as:
–2 D2 D0 e s ...... (36)
Step 6: Calculate the fraction of time k due to fades of duration less than Dt as:
–1 ln(D ) – ln(D ) D D (1 – ) Q t 0 0 2 k 1 ...... (37) ln(Dt ) – ln(D2) Dt Q where:
Q:standard cumulative distribution function for a normally distributed variable:
1 2 1 – x Q(z) e 2 dx ...... (38) 2 z Step 7: Calculate the probability of occurrence of fade events of duration d longer than D given that attenuation a is greater than A as:
– For 1 D Dt P(d D|a A) D ...... (40)
ln(D) – ln(D ) Q 2 – For D Dt P(d D|a A) D ...... (41) t ln(D ) – ln(D ) Q t 2 Step 8: Calculate the cumulative probability of exceedance, i.e. the total fraction of fade time due to fades of duration d longer than D:
1 – D For 1 D D F(d D|a A) 1 – k ...... (42) t D t
ln(D) – ln(D ) Q 0 For D Dt F(d D|a A) (1 – k) ...... (43) ln(D ) – ln(D ) Q t 0
Step 9: If required, the total number of fades of duration d longer than D for a given threshold A can be calculated from:
N(D,A) P(d D|a A) Ntot (A) ...... (44)
CHAPTER FOUR
4.0 DATA ANALYSIS
Data collected at the eight TRODAN stations in Nigeria as presented in Table 2 were processed and analysed. The rain rate distribution of the stations showing the average monthly rainfall accumulations during the observation period is as shown in Figure 4.1. The average monthly rainfall depends on the effects of movement of the Intertropical Convergence Zone (ITCZ). In wet season, the ITCZ discontinuity follows the sun northward, as a result, more and more of the country comes under the influence of the moisture-laden tropical maritime air. As wet season rounds off, the zone shifts southward, bringing an end to the rainy season (Ojo and Falodun,
2012). Nigeria has two seasons, dry (Nov., Dec., Jan., and. Feb) season, and wet (the rest of the calendar year) season. Rainfall usually falls during the wet season and during this period the
ITCZ moves across the country.
Figure 4.1: The average monthly rainfall accumulations during the observation period
4.1 RESULTS
STATISTICS OF RAIN RATE
It is well known that rain intensity appears as one of the important parameters for the design of microwave propagation link as this parameter directly influences the cumulative statistics of the rain attenuation. The rain rate computations were carried out using Chebil,1999,
Moupfouma,1995 and ITU P1144-3and P837-4 rain rate models because these models have been known to give good results for predicting the rain rate for tropical regions while in the rain attenuation computations, the Moupfouma and ITU P838-3 models were employed because they have been known to give good rain attenuation prediction for tropical regions.The Moupfouma model was used to determine the percentage of exceedances at 0.001%, 0.01%,0.1% and 1.00%.
Figure 4.2 shows combined rain rate of all location while figure 4.3 shows the individual rain rates of the locations.
Figure 4.2: Rain rate for the eight TRODAN stations in Nigeria
(a)
(b)
Figure 4.3: Rain rate percentage of exceedance in (a) Mowe (b) Minna
(a)
(b)
Figure 4.4: Rain rate percentage of exceedance in (a)Lagos (b) Nsukka
(a)
(b)
Figure 4.5: Rain rate percentage of exceedance in (a) Port Harcourt (b) Yola
(a)
(b)
Figure 4.6: Rain rate percentage of exceedance in (a) Ayingba (b) Akure
4.2 RAIN FADES DEPTH
Following the approach presented in section 3.4, in order to determine the depth of fade (LRain), the specific attenuation ( R ) (dB/km) and the path length (DRain) through the troposphere in km were calculated for C, ku, and ka bands in vertical, Horizontal and circular polarization for all location. They are as presented in table 4.1 and 4.2. The graphs were plotted in figure 5.
4.3 ATTENUATIONS
The ITU’s long-term rain attenuation statistics have been analyzed to determine the amount of rain fading for different availabilities. The geo-characteristic parameters for each location under study are shown in Table 4.3. In this table the attenuations (in dB) that are expected for unavailability of 0.01% of the time under consideration and the effective path lengths (in km) for
frequencies ranging from C-band up to Ka-band for circular, horizontal, and vertical polarizations are shown. The graphs were plotted for all locations as shown in figure 4.5 and 4.6.
44 FADE DURATION
Table 4.4 to 4.11shows the number of fade duration for which duration exceeds threshold at
1,3,6,9,121nd 18 dB levels, and the graphs are shown in figure 4.10.
The main objective of this section is to estimate of the average number of events per year of rain attenuation greater than a given threshold. The duration of a fade is defined as the time interval between two consecutive crossings of the sameattenuation threshold. Fade duration statistics are usually presented as conditional distributions of the number of fade events exceeding certain durations, given that a specified fade threshold has been exceeded. This representationprovides information on the number of outages and system availability due to propagation on a link, given a fademargin and an availability specification. The ITU-R defines duration statistics by two different types of conditionalCDF [4]: (a) the probability of occurrence of fades of duration d longer than D (s), given that the attenuation a is greater Using the ITU-Model discussed previously, in the methodology consisting of a log-normal distribution function for long fades and power-law function for short fades. The boundary between short and long fades is given by the threshold durationDt.
(a)
(b)
(c)
Figure 4.7: Fade depth of all location for (a) vertical polarization (b) horizontal polarization (c) circular polarization
STATION FREQ h h R L L ϒ L r s 0.01 s G Rain
6 4.878 0.019 110.7246 5.400381 2.066618 3.77139 20.36694
8 4.881 0.019 110.7246 5.400381 2.066618 2.076922 11.21617 12 4.881 0.019 110.7246 5.400381 2.066618 5.196604 28.06364 LAGOS 16 4.881 0.019 110.7246 5.400381 2.066618 7.450446 40.23525 20 4.881 0.019 110.7246 5.400381 2.066618 11.5741 62.50455 30 4.881 0.019 110.7246 5.400381 2.066618 20.32951 109.7871 40 4.881 0.019 110.7246 5.400381 2.066618 26.48993 143.0557
6 4.852 0.01131 74.583 5.395364 2.073384 2.286701 12.33758
MOWE 8 4.852 0.01131 74.583 5.395364 2.073384 1.233718 6.656376 12 4.852 0.01131 74.583 5.395364 2.073384 3.224174 17.39559 16 4.852 0.01131 74.583 5.395364 2.073384 4.748467 25.61971 20 4.852 0.01131 74.583 5.395364 2.073384 7.551914 40.74535 30 4.852 0.01131 74.583 5.395364 2.073384 13.64046 73.59525 40 4.852 0.01131 74.583 5.395364 2.073384 18.31607 98.82186
6 4.878 0.01075 123.4714 5.246437 0.056399 4.17642 21.91132
PORT 8 4.878 0.01075 123.4714 5.246437 0.056399 2.411574 12.65217 HACOURT 12 4.878 0.01075 123.4714 5.246437 0.056399 5.95776 31.25701 16 4.878 0.01075 123.4714 5.246437 0.056399 8.483505 44.50817 20 4.878 0.01075 123.4714 5.246437 0.056399 13.10427 68.75073 30 4.878 0.01075 123.4714 5.246437 0.056399 22.82121 119.73 40 4.878 0.01075 123.4714 5.246437 0.056399 29.45691 154.5438
6 4.789 0.4454 4.83266 4.832665 1.525426 3.475441 16.77564
8 4.789 0.4454 4.83266 4.832665 1.525426 1.983754 9.586819 NSUKKA 12 4.789 0.4454 4.83266 4.832665 1.525426 4.981542 24.07412 16 4.789 0.4454 4.83266 4.832665 1.525426 7.164452 34.6234 20 4.789 0.4454 4.83266 4.832665 1.525426 11.16321 53.94806 30 4.789 0.4454 4.83266 4.832665 1.525426 19.64735 94.94906 40 4.789 0.4454 4.83266 4.832665 1.525426 25.65038 123.9597
6 4.786 0.234 90.1453 5.056228 1.795497 2.865036 14.48627
8 4.786 0.234 90.1453 5.056228 1.795497 1.587445 8.026483 12 4.786 0.234 90.1453 5.056228 1.795497 4.061729 20.53703
MINNA 16 4.786 0.234 90.1453 5.056228 1.795497 5.906571 29.86497 20 4.786 0.234 90.1453 5.056228 1.795497 9.291293 46.97889 30 4.786 0.234 90.1453 5.056228 1.795497 16.55385 83.70003
40 4.786 0.234 90.1453 5.056228 1.795497 21.90049 110.7339
6 4.8 0.18121 107.77 4.9622 1.778293 3.599203 14.48627
8 4.8 0.18121 107.77 4.9622 1.778293 2.008193 8.026483 AKURE 12 4.8 0.18121 107.77 4.9622 1.778293 5.038388 20.53703 16 4.8 0.18121 107.77 4.9622 1.778293 7.238281 29.86497 20 4.8 0.18121 107.77 4.9622 1.778293 11.26562 46.97889 30 4.8 0.18121 107.77 4.9622 1.778293 19.81999 83.70003 40 4.8 0.18121 107.77 4.9622 1.778293 25.86895 110.7339
6 4.8 0.18121 94.3269 4.943744 0.895856 2.911854 14.39546
8 4.8 0.18121 94.3269 4.943744 0.895856 1.695935 8.384269 YOLA 12 4.8 0.18121 94.3269 4.943744 0.895856 4.31431 21.32885 16 4.8 0.18121 94.3269 4.943744 0.895856 6.258166 30.93877 20 4.8 0.18121 94.3269 4.943744 0.895856 9.826053 48.57749 30 4.8 0.18121 94.3269 4.943744 0.895856 17.43563 86.1973 40 4.8 0.18121 94.3269 4.943744 0.895856 22.95946 113.5057
6 4.843 0.38819 94.3269 4.886028 1.896707 2.052041 10.02633
8 4.843 0.38819 94.3269 4.886028 1.896707 1.142304 5.58133 AYINGBA 12 4.843 0.38819 94.3269 4.886028 1.896707 3.003939 14.67733 16 4.843 0.38819 94.3269 4.886028 1.896707 4.4444 21.71546 20 4.843 0.38819 94.3269 4.886028 1.896707 7.098364 34.68281 30 4.843 0.38819 94.3269 4.886028 1.896707 12.87044 62.88534 40 4.843 0.38819 94.3269 4.886028 1.896707 17.3491 84.7682
Table 4.1: Ls= slant path length (km), hs= Antenna height (km), hR= Rain Height, Lg= Horizontal projection, ϒ= Specific attenuation, LRain = Rain loss/fade depth (dB)
SPECIFIC FADE DEPTH SPECIFIC FADE DEPTH SPECIFIC FADE DEPTH STATIONS FREQ ATTENUATION (VERTICAL ATTENUATION (HORIZONTAL (CIRCULARPOL (CIRCULAR (VERTICAL POLARIZATION) (HORIZONTAL POLARIZATIO) ARIZATION POLARIZATION) POLARIZATION POLARIZATION
6 2.286701 12.33758414 1.823406 9.837939001 2.055027 11.08762 8 1.233718 6.65637623 1.272944 6.86799617 1.253313 6.76208
12 MOWE 3.224174 17.39559217 3.313689 17.87855818 3.268884 17.63682 16 4.748467 25.61970768 4.893031 26.39968307 4.820576 26.00876 20 7.551914 40.7453456 7.812342 42.1504284 7.68168 41.44546 30 13.64046 73.5952461 14.05613 75.8379371 13.84799 74.71495 40 18.31607 98.82186381 18.76559 101.2471878 18.54074 100.034
6 3.77139 20.36694 3.015746 16.28619 3.393482 18.2361 8 2.076922 11.21617 2.144497 11.5811 2.110674 11.39844
12 5.196604 28.06364 5.3449 28.8645 5.270662 88.46358 LAGOS 16 7.450446 40.23525 7.686854 41.51194 7.568347 40.87196 20 11.5741 62.50455 11.99338 64.76882 11.78293 63.63231 30 20.32951 109.7871 20.96915 113.2414 20.64879 111.5113 40 26.48993 143.0557 27.15021 146.6215 26.81992 144.8378
6 4.17642 21.91132 3.617349 18.97819 3.89686 20.44463 8 2.411574 12.65217 2.462328 12.91845 2.436933 12.78522
12 PORTHARCO- 5.95776 31.25701 6.067936 31.83504 6.012802 31.54579 URT 16 8.483505 44.50817 8.658494 45.42624 8.57085 44.96642 20 13.10427 68.75073 13.41323 70.37166 13.25836 69.55915 30 22.82121 119.73 23.28685 122.173 23.05377 120.9501 40 29.45691 154.5438 29.93153 157.0339 29.69415 155.7885
6 3.475441 16.77564 2.98586 14.42966 3.230629 15.61255 8 1.983754 9.586819 2.027307 9.797296 2.005515 9.691982
12 4.981542 24.07412 5.077489 24.5378 5.029476 24.30577 NSUKKA 16 7.164452 34.6234 7.317608 35.36355 7.240898 34.99284 20 11.16321 53.94806 11.43526 55.26278 11.29888 54.6037 30 19.64735 94.94906 20.06421 96.96361 19.85556 95.95525 40 25.65038 123.9597 26.08264 126.0487 25.86644 125.0038
6 2.865036 14.48627 2.362831 11.9470 2.613907 13.21651 8 1.587445 8.026483 1.631103 8.24722 1.609255 8.13676
12 4.061729 20.53703 4.15951 21.0314 4.110572 20.7839 MINNA 16 5.906571 29.86497 6.063519 30.6585 5.984883 30.26093 20 9.291293 46.97889 9.571963 48.3980 9.43119 47.68624 30 16.55385 83.70003 16.99214 85.9161 16.7727 84.80659 40 21.90049 110.7339 22.36395 113.077 22.13213 111.9051
6 3.599203 14.48627 2.958779 14.68205 3.278954 16.27083 8 2.008193 8.026483 2.065257 10.24822 2.036698 10.1065 AKURE 12 5.038388 20.53703 5.163954 25.62457 5.101105 25.3127 16 7.238281 29.86497 7.438636 36.912 7.338235 36.41379 20 11.26562 46.97889 11.62135 57.66746 11.44289 56.78191 30 19.81999 83.70003 20.36434 101.0519 20.09177 99.69938 40 25.86895 110.7339 26.43263 131.164 26.15067 129.7649
6 2.911854 14.3954 2.625705 12.98081 2.768771 13.6881 8 1.695935 8.38426 1.720967 8.508021 1.708445 8.446115
12 YOLA 4.31431 21.3288 4.370124 21.60478 4.342202 21.46674 16 6.258166 30.9387 6.34762 31.38101 6.302842 31.15964 20 9.826053 48.5774 9.985734 49.36692 9.905758 48.97154 30 17.43563 86.1973 17.68369 87.42364 17.55957 86.81003 40 22.95946 113.505 23.22038 114.7956 23.08989 114.1505
6 2.052041 10.0263 1.747553 8.538594 1.899785 9.282403 8 1.142304 5.58133 1.167862 5.706207 1.155075 5.643729
12 AYINGBA 3.003939 14.6773 3.062619 14.96404 3.033257 14.82058 16 4.4444 21.7154 4.539335 22.17932 4.491795 21.94704 20 7.098364 34.6828 7.269818 35.52054 7.183885 35.10067 30 12.87044 62.8853 13.14608 64.2312 13.00812 63.55804 40 17.3491 84.7682 17.6494 86.23255 17.49921 85.50164
Table 4.2: Estimates of specific attenuation and fade depth for all polarizations
(a)
(b) (c)
Figure 4.8: Attenuation at 0.01% exceedance for all regions for (a) vertical polarization, (b)
horizontal polarization, and (c) circular polarization.
STATION FREQ LE A0.01 LE A0.01 LE A0.01 VERTICAL VERTICAL HORIZONTAL HORIZONTAL CIRCULAR CIRCULAR 6 1.430929 3.272106 1.621191 2.95609 1.518326 3.120202 8 3.121537 3.851097 3.076383 3.916063 3.098751 3.883705 12 3.494067 11.26548 3.450988 11.4355 3.472362 11.35075 MOWE 16 4.16391 19.77219 4.111705 20.1187 4.137631 19.94576 20 4.348673 32.8408 4.288591 33.50394 4.318427 33.17278 4.961306 67.6745 4.905643 68.95436 4.933272 68.31591 30 5.483352 100.4335 5.437301 102.0342 5.460178 101.2357 40
6 1.077624 4.064139 1.22685 3.699868 1.146073 3.889178 8 2.432437 5.051983 2.393507 5.132869 2.412781 5.092593 12 2.79812 14.54072 2.759804 14.75088 2.778805 14.64614 LAGOS 16 3.427018 25.53281 3.378431 25.9695 3.402543 25.75163
20 3.633962 42.05984 3.57692 42.89936 3.605229 42.48017 30 4.255756 86.51744 4.202439 88.12157 4.228891 87.32149 40 4.810674 127.4344 4.76639 129.4085 4.788382 128.424
6 1.038582 4.337556 1.128747 4.08307 1.081264 4.213535 PORT 8 2.295108 5.534824 2.271107 5.59221 2.283031 5.563593 HARCOURT 12 2.684179 15.9917 2.66017 16.14174 2.672111 16.06687 16 3.32478 28.20579 3.29392 28.52038 3.309275 28.3633 20 3.556862 46.61008 3.520299 47.21858 3.538492 46.9146 30 4.225236 96.42501 4.190914 97.59318 4.207989 97.01002 40 4.821781 142.0348 4.793318 143.4713 4.807488 142.7543
6 1.075226 3.736885 1.171833 3.498929 1.120847 3.62104 8 2.349626 4.661081 2.324845 4.713174 2.337154 4.687198 12 2.722653 13.56301 2.698145 13.6998 2.710332 13.63155 Nsukka 16 3.338188 23.91629 3.307248 24.20114 3.322642 24.05891 20 3.552505 39.65736 3.516124 40.20779 3.534226 39.9328 30 4.179202 82.11024 4.145232 83.17081 4.16213 82.64141 40 4.726673 121.241 4.698612 122.5522 4.712581 121.8977
6 1.211313 3.470454 1.349573 3.188812 1.275605 3.334313 8 2.655037 4.214725 2.620745 4.274705 2.637752 4.244815 12 3.025982 12.29072 2.992643 12.44793 3.0092 12.36953 MINNA 16 3.661639 21.62773 3.620331 21.95195 3.64086 21.79012 20 3.861915 35.88219 3.813833 36.50586 3.83773 36.19436 30 4.478033 74.12868 4.4333 75.33126 4.455525 74.73118 40 5.010399 109.7302 4.973411 111.2251 4.991802 110.4792
6 1.069048 3.847722 1.195794 3.53809 1.127863 3.698209
8 2.37181 4.763051 2.339226 4.831103 2.355379 4.797197 AKURE 12 2.730714 13.7584 2.698667 13.93579 2.714577 13.84734 16 3.336992 24.15409 3.296585 24.5221 3.316659 24.33842 20 3.538634 39.8649 3.491241 40.57293 3.514787 40.21932 30 4.139071 82.03634 4.094912 83.39018 4.116845 82.71469 40 4.668783 120.7765 4.632254 122.4427 4.650413 121.6114
6 1.224403 3.565282 1.296444 3.40408 1.259077 3.486097 8 2.607325 4.421853 2.589372 4.456224 2.59831 4.439069 12 3.020165 13.02993 3.002468 13.12116 3.011284 13.0756 YOLA 16 3.688438 23.08285 3.666358 23.27265 3.677362 23.17783 20 3.924392 38.56128 3.898508 38.92947 3.911409 38.74547 30 4.611931 80.41192 4.587797 81.12918 4.599824 80.77093 40 5.198313 119.3505 5.178446 120.2455 5.188351 119.7985
6 1.464874 3.005982 1.59653 2.790021 1.526959 2.900893 8 3.087245 3.526573 3.05686 3.56999 3.071955 3.548338 12 3.468166 10.41816 3.439183 10.53291 3.453598 10.47565 AYINGBA 16 4.123527 18.3266 4.088748 18.5602 4.106055 18.44356 20 4.313355 30.61777 4.273359 31.06654 4.293265 30.84232 30 4.922991 63.36106 4.886064 64.23258 4.904436 63.79749 40 5.42981 94.20231 5.399426 95.29662 5.414551 94.75036
Table 4.3:Attenuations (in dB) that are expected for 0.01% of the time and the effective path
lengths (in km) for frequencies ranging from C-band up to Ka-band for circular, horizontal, and
vertical polarizations
(a)
(b)
(c)
Figure4.9: The effective path length for attenuations at 0.01% of all locations for (a) vertical polarization, (b) horizontal polarization, and (c) circular polarization
(a)
(b)
Figure 4.10: Attenuation at C-band for horizontal polarization for all locations at (a) 6 GHz and (b) 8 GHz.
(a)
(b)
Figure 4.11: Attenuation at Ku-band for vertical polarization for all locations at (a) 12 GHz and (b) 16 GHz.
(a)
(b)
Figure 4.12: Ka band for horizontal polarization for all locations at (a) 30 GHz and (b) 40 GHz
(a)
(b)
Figure 4.13: Fade duration grouping by attenuation levels in (a) Port Harcourt
(b) Lagos
(a)
(b)
Figure 4.14: Fade duration grouping by attenuation levels in (a) Mowe (b) Minna
(a)
(b)
Figure 4.15:Fade duration grouping by attenuation levels in (a) Akure (b) Nsukka
(g)
(h)
Figure 4.16: Fade duration grouping by attenuation levels in (a) Yola(b) Ayingba
Duration (sec) 1dB 3dB 6dB 9dB 12dB 15dB 18dB
10 1456 1179 428 120 70 30 10 40 427 151 90 45 20 9 5 70 196 100 45 31 12 5 1 180 150 76 38 12 5 2 0 300 111 51 32 10 1 0 0 1600 16 4 0 1 0 0 0 2400 8 0 0 0 0 0 0 3600 2 0 0 0 0 0 0 5000 0 0 0 0 0 0 0
Table 4.4: Number of events for which duration exceeds threshold in Lagos
Duration (sec) 1dB 3dB 6dB 9dB 12dB 15dB 18dB
10 2779 1271 1108 680 475 211 50 40 722 530 304 101 85 42 8 70 258 138 92 32 11 5 1 180 75 20 11 5 0 0 1 300 28 9 0 0 0 0 0 1600 7 3 1 1 1 0 0 2400 3 1 0 0 0 0 0 3600 1 0 0 0 0 0 0 5000 0 0 0 0 0 0 0
Table4.5: Number of events for which duration exceeds threshold in Port Harcourt
Duration sec) 1dB 3dB 6dB 9dB 12dB 15dB 18dB
10 418 226 156 112 82 43 16 40 286 161 109 73 41 12 7 70 134 76 31 17 8 4 1 180 101 12 9 5 1 0 0 300 92 39 1 0 1 1 0 1600 12 1 0 0 0 0 0 2400 8 0 0 0 0 0 0 3600 1 0 0 0 0 0 0 5000 0 0 0 0 0 0 0
Table4.6: Number of events for which duration exceeds threshold in Yola
Duration (sec) 1dB 3dB 6dB 9dB 12dB 15dB 18dB
10 1025 488 207 134 77 25 13 40 277 153 65 48 20 9 6 70 162 106 40 18 10 6 1 180 131 64 38 16 6 2 1 300 103 45 32 10 2 1 0 1600 14 3 0 0 0 0 0 2400 7 0 0 0 0 0 0 3600 2 0 0 0 0 0 0 5000 0 0 0 0 0 0 0
Table 4.7: Number of events for which duration exceeds threshold in Mowe
Duration (sec) 1dB 3dB 6dB 9dB 12dB 15dB 18dB
10 748 560 256 150 91 30 5 40 621 320 210 110 74 15 0 70 549 281 169 92 41 11 0 180 263 156 82 30 16 5 0 300 107 49 21 18 7 5 0 1600 20 7 7 6 4 0 0 2400 8 5 7 5 0 0 0 3600 5 0 0 0 0 0 0
Table4.8: Number of events for which duration exceeds threshold in Minna
Duration (sec) 1dB 3dB 6dB 9dB 12dB 15dB 18dB
10 309 112 37 20 20 11 15 40 121 74 20 10 13 9 7 70 44 21 8 4 5 6 1 180 17 12 6 3 1 1 0 300 7 5 2 0 0 0 0 1600 2 1 0 0 0 0 0 2400 0 0 0 0 0 0 0 3600 0 0 0 0 0 0 0 5000 0 0 0 0 0 0 0
Table4.9: Number of events for which duration exceeds threshold in Akure
Duration (sec) 1dB 3dB 6dB 9dB 12dB 15dB 18dB
10 887 549 284 169 118 71 42 40 510 361 220 107 49 19 9 70 250 105 107 51 22 14 1 180 149 60 48 20 8 3 1 300 57 33 15 7 5 1 0 1600 32 10 1 0 0 0 0 2400 10 7 0 0 0 0 0 3600 2 0 0 0 0 0 0 5000 0 0 0 0 0 0 0
Table 4.10: Number of events for which duration exceeds threshold in Ayingba
Duration (sec) 1dB 3dB 6dB 9dB 12dB 15dB 18dB
10 1305 1025 315 111 66 28 10 40 314 145 92 38 15 10 1 70 150 62 30 12 5 1 1 180 125 50 22 15 5 0 0 300 74 35 10 1 0 0 0 1600 15 5 1 0 0 0 0 2400 8 0 0 0 0 0 0 3600 2 0 0 0 0 0 0 5000 0 0 0 0 0 0 0
Table 4.11: Number of events for which duration exceeds threshold in Nsukka
CHAPTER FIVE
5.0 DISCUSSION OF RESULTS
From the monthly rainfall distribution at the locations under consideration as shown in Figure
4.1, it is seen that both Port Harcourt andLagosrecorded their peak average monthly rainfall accumulation in the month of July and June with 511 and 345 mm respectively, this is due to the fact that both location are in the coastal region. Redemption camp (Mowe), Akure and Nssuka recorded their peaks in June, September and June with 88, 234 and197mm respectively while the
Northern part of the country comprising of Yola and Minnarecorded their peak in September and
August with 224 and 256mm respectively.
The average cumulative distributions of rain rate over the observation period for the eight locations in Nigeria as derived are shown in Figure 4.2. The rain rates were plotted for other percentage of time 0.001% to 0.01%, 0.1% and 1% of an average year. This corresponds to 5.26 minutes to 8.76 hours of exceedance of the indicated one-minute rainfall rates in an average year.
It is seen that Port Harcourt had the highest cumulative distribution of 123mm/h followed by
Lagos and Nsukka with110mm/h and 106 mm/h respectively, followed by Akure (107 mm/h,
Nsukka (106 mm/h),Yola (94 mm/h), Minna (90 mm/h), Mowe (74) and Ayingba had the lowest values wth 70 mm/h.The result shows thatthe Chebil rain rate model is a better model because it converts the rain rate of any location to its equivalent one-minute rain rate value irrespective of the integration time the data was measured. Consequently the Moupfouma’s attenuation model
which uses the Chebil’s rain rate as input also provides a good estimation for rain attenuation at any location.
Fade depth is the reduction ofsignal strength from the normal received level, measured in dB
Figure 4.6 shows the various fade depths of each of the station from frequencies ranging from the C- band to Ka –band.It indicated the effect of polarization on communication links in Nigeria since this is a consideration for antennal polarity needed by system designers. The results show that the fade depth is most severe in Port Harcourt, followed in descending order, by Lagos,
Akure, Yola, Nsukka ,Minna and Ayingba.Port Harcourt has the highest rain rate and was seen to have the highest fade depth. It can be seen from all the polarizations that Ayingba station that has the lowest rain rate will experience least fade for the same frequencies as other stations. It can also be seen graphically that the higher the frequency, the higher the signal fade that will be experienced on communication link. Fade depth ranges between 0.27 dB in the North East(Yola) to 1.49 dB in the South South (Port Harcourt) and the South West (Lagos, Akure and Ayingba) regions.
Rain attenuation on earth-space path and the effective path length have been calculated for frequencies 6 -40 GHz for rain rates exceeded for 0.01% of timeas shown Table 4.3 and the graphs are plotted in Figure 4.7. The result of this study shows that the rain attenuations for vertically and circularly polarized signal are less than that of the horizontal polarization at all the frequencies and elevation angles investigated. It is also observed that rain attenuation is less severe in the Northern part but is more severe in the southern part of Nigeria, with Port Harcourt,
Lagos and Nsukka having the highest rain impairment.The results also suggest that there will be total fade out of signals at 0.01% unavailability at Ku and Ka band for all elevation angles in all the 8-stations during rainfall. It means for 99.99% (about 53 minute outage in a year).
The effective path length for each region is determined and its dependency on frequency and elevation angle is evident. This length is used instead of the actual geometric length due to the non-uniformity of rain density as the signal travels through a rainy medium. The resultof the effective path lengths 0.01% unavailability as seen in Table 4.3 shows that the slant path length
LS and the horizontal projection length LG were longest in Mowe,followed by Lagos, Port
Harcourt, Minna, Yola, Akure, Nssuka and Ayingba. The reason for Mowe LS and LG being the longest and AyingbaLS and LG being the lowest was due to their geometrical height above the mean sea level.
Figure 4.8 also shows that the effective path lengths through rain increases with increasing frequency for all the locations and was highest at Ka-band.
It is seen through Equation (13) that the effective path length is directly proportional to the rainfall attenuation. However, for the satellite under consideration and the region of study, the contribution to the overall attenuation due to the effective path length of little effect due to other dominant factors such as frequency and local rain rate.
Availability is the ratio typically associated with the percentage of the total time a service is enabled or being used, or is available to be utilized during a given interval to the length of the interval. Figures 4.9, 4.10 and 4.11 shows the availabilities ranging from 95% to 99% of the time. Evidently, as the availability increases, so does the required rain fade margin. The variability of rainfall attenuation with availability is location dependent as well as frequency
dependent. It also follows, given the local rain rates, that Port Harcourt experiences the highest rainfall attenuation within the range of the probabilities considered. The results are consistent with the notion that higher rain rates lead to high fade margins. The total amount of power needed to overcome rain effects for a given availability varies significantly with propagation path length and frequency of the links as well as its location also varies from year to year for the same link. It is obviousthat the cumulative distribution of measured rain induced attenuation shows that there are some regions where the probability of rain attenuation decreases rapidly (i.e
Yola and Ayingba), and if we can increase the fade margin so that it exceeds the highest rain attenuation recorded, the links will be outage-free.
Rain Fade duration
Table 4.4 to 4.11 shows the number of events for which duration exceeds threshold at 1, 3, 6, 9,
12, 15 and 18 dB levels in all location. From this table,it is seen that when the attenuation is increased the time duration will decrease in all the location. This is due that the heavy rains which cause high attenuation levelsat short duration. It is because the higher attenuation depends on rain drops size and also the rain intensity.
Figure 4.12 to 4.15 shows the number of events grouped by exceeding levels versus duration (s) for all the locations. Thenumber of events of attenuation exceeding 1dB level is higher than other levels, and also the duration of fade events is longer. This is due for attenuation exceeding 18dB level; it needs higher rain intensity and longer raining time compared to attenuation exceeding
1dB level.
CONCLUSIONAND RECOMMENDATIONS
This work has provided information on features of rain fades in Nigeria. It is found that June,
July, August, September and October are months of heavy and intense rain in Nigeria. The probabilities of the fade occurrence for these months are high.
It can be concluded that as frequency increase from C band to Ka-band, the attenuation increases, and so also the depth of signal fade.
Considering all the locations under study, Ayingba and Yola require lower fade margin for satellite link design purposes at all frequencies and percentage availabilities. On the other hand,
Port Harcourt and Lagos requires the highest fade margin at all frequencies and percentage availabilities.
The result of this study will assist system designers in the employment of mitigation techniques for designing reliable broadband communication link.A beneficial solution couldbe by integrating Adaptive Transmit Power Control (ATPC) with thebroadband link transceiver. The transmit power will be automaticallyadjusted by referring to the loss in receive signal level
(RSL). The rainfading can be therefore be compensated by higher power transmission (Yen-Wu-
Chen, 2015).
During the clear weather, the transmit power is reduced back to anominal lower level so that excessive interference will not be generated.By this way, the broadband service providers will be able to ensure service availability during rain events.
It is recommended that when communication satellites are being produced for areas with high rain fall rate, the satellite engineers should put into consideration the differences in degree of attenuation values from one location to another because these values represent an uncertainty in the design of communication link and this affects service availability and can lead to interruption of communication link performance.
Another feature of rain fade statistics is the evaluation of fade slope, this require further studies.
Also, the height of TRODAN rain gauge satellite should be increased from a few centimeters to a minimum of 100 metres in altitude from the ground so as to avoid the blockage of the gauge filter caused by dead grass and debris.
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