INTELLIGENT WEATHER AWARENESS TECHNIQUE FOR …

Vol.107 (3) September 2016SOUTH AFRICAN INSTITUTE OF ELECTRICAL ENGINEERS136

INTELLIGENT WEATHER AWARENESS TECHNIQUE FOR MITIGATING PROPAGATION IMPAIRMENT AT SHF AND EHF SATELLITE NETWORK SYSTEM IN A TROPICAL CLIMATE. I.A. Adegbindin*, P.A. Owolawi** and M.O. Odhiambo*** * Dept. of Electrical and Mining Engineering, University of South Africa, Private Bag X6, 1710 Florida Pretoria, South Africa ** Dept. of Electrical Engineering, Mangosuthu University of Technology, P. O. Box 12363, Jacob, 4026 Durban, South Africa *** Dept. of Process Control & Computer System, Vaal University of Technology, Private Bag X021, Vanderbijlpark 1911, South Africa Abstract: This paper presents an Intelligent Weather Awareness Technique (IWT) based on fuzzy logic to achieve optimum signal quality when the impact of weather attenuations become manifest in Earth-satellite link. An adaptive measure of a decision support system is adopted using the point rainfall rate to predict rain-induced attenuation over three locations in the tropical region of Nigeria based on Moupfouma rain rate and attenuation model. In order to impact good Quality of Service (QoS) in the overall design of satellite communication networks, considerable efforts have to be made, such as the use of appropriate forward error correction codes, the choice of modulations and the range of uplink/downlink power controls. The IWT interacts with the input parameters to optimise the Signal to Noise Ratio (SNR) values while the input parameters are varying. The results show that in all three locations considered, a minimum fade margin of about 12 dB on the uplink and ~ 8 dB on the downlink may be allowed at the 99.5% availability for a worst case scenario at Ka-band. Analysis based on SNR and consumed power at the same weather conditions confirms the suitability of introduction of fuzzy logic for optimum availability of good QoS in a weather impaired satellite system. The overall results would provide tolerance margins needed for better QoS, especially during bad weather in the study locations. Key words: SNR, tropical weather, Intelligent Weather Awareness Technique, satellite network.

1. INTRODUCTION

Rain Induced Attenuation (RIA) is one of the major propagation impairments for radio wave propagation transmitting at high frequency bands above 10 GHz. At such frequency bands, rain attenuation absorbs and scatters radio waves, leading to signal degradation. The severity of the impairment becomes even more pronounced for frequencies as low as 7 GHz in the tropical and equatorial regions where intense rainfall events are common [1]. The significance of this event is usually observed at the receiving terminal by variations in the desired signal level. Precise technical knowledge of rain attenuation is therefore very important for designing and planning reliable satellite communication networks [2]. Common rain attenuation prediction methods require one-minute rain rate data at the location of study as recommended by the International Telecommunication Union (ITU), which is scarce globally with worst case in tropical regions [3]. The available rain data are usually of longer integration time like 5-minutes, 10-minutes, 30-minutes, hourly and daily data. These data are available at different weather or meteorological stations at different locations. Many investigators including Segal [4] proposed a conversion factor )(P for converting the raw data collected from measuring rain gauge to an equivalent 1-minute and note that P is defined as the percentage of exceedance with range over %03.0%001.0 P , in the

case of Rice Holmberg [5], a physical model was proposed based on cumulative distribution of rain rate. Two modes of methods were proposed: mode 1 deals with convective type of rain )( 1M while mode 2 deals with other types of rain )( 2M . The contribution of Kitami model [6] to integration time conversion is based on semi-empirical method by using the Kitami Institute of Technology(KIT) database which allow the variables such as latitude , longitude rain rate values at 0.01% and 0.001% of exceedences to be input to the general semi-empirical expression to yield 1-minute equivalent rain rate, and contribution of Moupfouma and Martins model [7] focus on three regions of climatic conditions which are tropic, temperate and sub-tropic and the model is more popular than previously mentioned model because of the inclusion of climatic parameters. The expressions and details of the above listed models and other related models are presented in the work by Owolawi [8] .The main purpose of all these models is to devise procedures to convert the available longer integration data to the required one-minute data. However, not all the models can fit into the tropical region based on the data from the temperate region used in developing some of the models. Invariably, some of the models do not give an accurate prediction of rain rate when applied to the tropical and equatorial regions [3]. Recent studies from the tropical region also show that rain rate distribution is better

Vol.107 (3) September 2016 SOUTH AFRICAN INSTITUTE OF ELECTRICAL ENGINEERS 137

described by a model which approximates a log-normal distribution of the low rain rates and gamma distribution of the high rain rate [3, 9]. Among the existing models, Moupfouma and Martins model (henceforth referred to as MM model) [7, 10] has been identified to fit into this factor [3, 11]. The model can be adopted for both tropical and temperate climates. Once the one-minute rain rate is obtained, the expected rain-induced attenuation that could be encountered along the satellite link can be estimated. Subsequently, the appropriate methods for mitigating the propagation impairment will be applied. Some of these include adaptive coding, antenna beam shaping, site diversity, and uplink power control [12]. Uplink power control has been identified as one of the most cost-effective attenuation mitigation techniques as it enhances link availability and performance [2, 12]. In tropical climates like Nigeria (with emphasis on some selected locations), rain is the predominant parameter among the hydrometeor; hence the effect of rain is majorly considered in this work. In this paper, we have adopted an Intelligent Weather Awareness Technique (IWT) to estimate the tolerance amount of SNR needed to achieve optimum signal quality when the impact of bad weather becomes manifest in Earth-satellite link. Results are presented in 3D forms as a function of propagation impairments for both propagation angle and rainfall rate at a Super High Frequency (SHF) and Extremely High Frequency (EHF) bands for three locations: Port Harcourt, Lagos and Akure. The data obtained were fed to the techniques adopted with a mechanism to better estimate satellite networking parameters such as link and queuing characteristics. The derived parameters would enable the Earth-satellite system to maintain QoS by adaptively adjusting satellite signal power, modulation, coding and data rate for unpredictable weather conditions in the region.

2. SITE, DATA AND METHODOLOGY In this section, we present a brief report on the characteristics of the locations considered, nature of the data used and methodology adopted based on this work. 2.1 Site and Data Three locations were considered for this work: Port Harcourt, Lagos and Akure. Each of these chosen locations represents different climatic regions of the country with some prominent record of the high amount of rainfall [3, 11]. Table 1 presents the climatic characteristics of the locations studied. In Nigeria, the coastal region received an annual average rainfall of about 3000 mm, while the arid/savannah region received an average accumulation of less than 1000 mm. The seasonal northward and southward

oscillatory movement of the InterTropical Discontinuity (ITD) largely dictates the weather pattern. Table 1: Climatic characteristics of the sites

Location Long (◦N), Lat (◦E)

Annual rainfall amount (mm/yr)

Climatic region

Port Harcourt (PH)

7.0, 4.2 2803 Coastal

Lagos 3.2, 6.3 1425 Semi-Coastal

Akure 8.3, 11. 58

1485 Rain Forest

The moist south-westerly winds from the South Atlantic Ocean, which is the source of moisture needed for rainfall and thunderstorms to occur, prevail over the country during the rainy season (April – October), while the north-easterly winds which raise and transport dust particles from the Sahara desert prevail all over the country during the dry/Harmattan period (November – March) [13]. Ten years (2002-2012) of archived rain rate data obtained from the Nigerian Meteorological Agency (NMA) for the three locations considered were processed to obtain the needed data of one-minute integration time. The network of rain gauges usually used by the NMA is comprised of a standard funnel of 127 mm in agreement with the World Meteorological Organization (WMO). Detailed descriptions are available in [3, 11] and not repeated here for the sake of brevity.

Methodology

(i) Estimation of rainfall rate of one-minute integration

time

The 10 years’ rain rate data collected from NMA were applied to the MM model [7]. The model can be expressed as [7]:

rRwrR

rRPz

01.0

01.04 exp1

10)( (1)

Where: r (mm/h) represents the rain rate exceeded for a fraction of time; R0.01 is the rain intensity exceeded during 0.01% of time in an average year (mm/h); and taking into account the shape of cumulative distribution of rain rate, parameter z is given by [7]:

01.001.0

01.0 1lnR

rR

Rrz (2)


The parameter w in Equation (1) governs the slope of rain rate cumulative distribution and depends on the local climatic conditions and geographical features, for tropical and sub-tropical settings [7]:

01.001.0

exp10ln4R

rR

w (3)

Where:

=1.066 and = 0.214.

The MM model requires three (3) parameters: λ, γ and R0.01. The first two parameters have been provided. To get the estimate value of R0.01, Moupfouma [10, 14] introduced a relationship whereby rain rate at different integration times is converted to one-minute equivalent at 0.01% exceedance of rain rate. The power law relationship of the model is given by [10]:

01.0minmin)1( RP (4)

Where:

061.0(min)987.0 (5) (5)

The equation (1) to (5) are valid within an integration time of 1min. < < 1 hr. Rain rate cumulative distribution of one-minute integration time is therefore obtained using Equations (1) to (5). (ii) Estimation of Rain-Induced Attenuation (RIA) The one-minute rain rate obtained based on Equations (1) to (5) is then applied to the Moupfouma rain attenuation model to estimate the RIA for these regions. The Moupfouma rain attenuation for terrestrial satellite links is given by [15]:

sm

s

Sp LLp

LRRpRudBA 36.0

25.001.0

01.0/1

38.0/)(

(6)

Where:

m = 1 + 1.4.10-4 f 1.76 Loge (Ls); f is frequency in GHz; and Ls is the slant path length in km. = 0 if Ls < 5 km, f < 25 GHz and 0.03 in all other cases. The values of u (p) for different regions of the world are stated in [16]. R0.01 and Rp are rainfall rates corresponding to rain rates of

0.01 and p percent of the time. The slant path length Ls, is given as follows:

s

sR

sRS L

HH

HHL

sin8500

2sin2

1

2

(7)

Where:

HR = 5.0 for 0o ≤ < 23o HR = 5.0 – 0.075 (-23), for ≥ 23o (8)

Where HS is the station height above the sea level, is a path elevation angle measured in degree and HR is height of the 0o Isotherm. The slant-path length, Ls, below the freezing rain height is obtained as follows:

sinSR

S

HHL

(km) (9)

The rain intensity, R0.01 (mm/h), exceeded for 0.01% of an average year obtained using Equation (5) is then obtained to deduce the specific attenuation, R (dB/km):

01.0kRR (10)

The values of k and α are dependent on the frequency, raindrop size, distribution, temperature, rain and polarization. Their values can be taken from [17]. (iii) Estimation of SNR The performance of a communication system is estimated based on the achievable signal-to-noise (SNR) at the receiver. The term SNR (in dB) refers to the estimation of signal strength as a function of signal degradation and background noise. In this work, SNR is analogous to Carrier-to-Noise ratio (C/N) in order to refer to receive carrier power. This power can be expressed as [18]:

2

0

4

lKTBLGGP

NC

NS

sys

rtt

(11)

Where

Pt = transmitting power (W); o = free-space wavelength (m); Gt and Gr are transmitting and receiving antenna gain respectively; K is the Boltzmann’s constant = 1.38x10-23 J/K; B = bandwidth (Hz); Lsys is the system loss in ratio (unit less); and l is the range (m). T is the system effective noise temperature (K) which is defined as:


RA TTT (12)

Where:

AT is antenna noise temperature( external noise) and RT is the receiver noise temperature (internal noise) , both are in the unit of Kelvin. It must be noted that RT is often expressed as the receiver’s noise figure in dB. The KT rN

R 290).110( 10/ , where in this work the low-noise-amplifier (LNA) considered has a typical noise figure ranges dBtoNr 27.0 . Also the parameter Lsys is related to the atmospheric loss, polarizer loss and the total attenuation. Note that the relationship between the carrier frequency (in Gigahertz) and wavelength in meter is expressed as:

)(3.0)( GHzfm (13)

(iv) Method adopted for satellite application Since the concern of the paper if on satellite link, the transmitting side of the satellite is expressed as:

WGPEIRP TT , (14)

Where:

In the receiving side:

1,/

K

TTG

TGRA

T (15)

The terminologies EIRP and TG / are the equivalent isotropic power and figure of merit respectively. Considering equation (14) and (15), it is possible to re-write equation (11) as:

BLKLTGEIRP

NC

NS

SysF

/. (16)

To express the equation (16) in decibel, the equivalent expression is presented as [18]:

)./(60.228).(

)()(

)/(/)()(

HzKdBWHzdBB

dBLdBL

TdBTGdBWEIRPdBNC

NS

Sysf

(17)

Equation (17) is employed to estimate the signal to noise ratio of the system. To account for the increase in SNR to achieve better system performance, the method used by [19] has been used based on the aforementioned equations. (v) Theory of Fuzzy-Logic Controller Fuzzy logic controller is a rule driven system where the controller uses fuzzy logic procedures to simulate human thinking in dealing with a complex system. Fuzzy logic controller is a better tool in dealing with processes that are too complex to analyse, especially when the available sources of information are interpreted quantitatively and uncertain in classification. The same situation is observed when the transmitted signal are mixed with several impairments and employed multiple mitigation techniques are available. Fuzzy logic controller is a good candidate to handle this complex situation because it consists of four compositions which are fuzzifier, rule base, fuzzy interference and defuzzier. The application of fuzzy logic controller adopted in this paper is detailed the contribution of Amruta et al. [20], [21] and the modification is in the area of input variable and defuzzification where the gravity method is considered.

(vi) Optimisation of the Network The reliability of a network is quantified by the percentage of time that the link between transmitter and receiver can be established [22]. In order to achieve optimal performance along the link, we applied IWT through a Fuzzy logic algorithm. The schematic diagram for different stages in the intelligent technique is presented in Figure 1. The algorithm iteratively tuned the IWT based on the SNR values to adjust the weighted modulation to its optimal values. This can be achieved depending on the following: the weather conditions, pre-set tolerance margins and the configuration settings. According to Harb et al. [19], a better SNR can be achieved by considering the total attenuation that could be encountered along the space to Earth propagation link among other parameters needed for the intelligent control through Fuzzy logic algorithm. The intelligent control is therefore designed to easily check against each of the input signal parameters (frequency, adjusted SNR, propagation angles, size frame etc.) in the database and make comparison with the threshold value. In the first stage, the technique held input signal parameters such as frame size, propagation angle and frequency, and the estimated SNR values that were compared against the threshold level (-30 dB or 0 dBm) in a single database as adopted by Herb et al. [12, 19]. The expectation is that the result obtained should either be greater than or equal to the threshold level. Depending on the SNR values, the algorithm will decide to increase


relative power transmitted for a maximum limit of -30 dB (0 dBm), or to skip to the next step. In the next stage, SNR value will be checked among modulation and assigned coding values. If this value can be reached by using any of the modulation/coding selections as presented in Table 2, then the system will proceed to the final stage.

Figure 1: The schematic diagram of the intelligent technique for satellite system

Table 2: Modulation/Coding selection needed in order to

adjust SNR

Modulation

LDPC Code

Identifier

Es/N0(dB) Measured/ Estimate

Relative Power (dB)

Rain Rate (mm/hr)

QPSK 1/4 -1.93 -52 9.92 QPSK 1/3 1.72 -51 9.10 QPSK 2/5 1.81 -48 8.41 QPSK 1/2 2.23 -49 7.46 QPSK 3/5 2.66 -47 6.75 QPSK 2/3 3.07 -48 6.97 QPSK 3/4 3.49 -48 5.31 QPSK 4/5 3.88 -46 4.87 QPSK 5/6 4.17 -45 4.53 QPSK 8/9 4.91 -44 3.87 8PSK 3/5 4.17 -41 4.32 8PSK 2/3 4.83 -43 3.60 8PSK 3/4 5.61 -41 2.82 8PSK 5/6 6.76 -40 2.01 8PSK 8/9 7.64 -39 1.32 16PSK 2/3 5.94 -42 2.21 16PSK 3/4 6.67 -38 1.56 16PSK 4/5 7.21 -40 1.15 16PSK 5/6 7.61 -36 0.88 16PSK 8/9 8.60 -34 0.32

In the final stage, the technique will check among different SNR achieved outputs and make decisions based on the intelligent controller according to available parameters and necessary requirements. The given feedback will keep interchanging until a suitable value and optimum level are reached. Thus, this system can also change data rate, frame size, and frequency in order to adaptively adjust SNR by transmission rate in cases such as unpredicted bad weather conditions, by using Refresh Duration that is located in the first stage. The general expression that incorporates the transmission rate with respect to symbol energy-to-noise power density is presented in [18]:

)(| dBRNC

NE

sdBO

s (18)

Where:

sR is a transmission rate measured in symbols per

second and O

s

NE

determine the bit error rate of

transmission scheme and the characteristic of a satellite

link can be estimated based on the set value of O

s

NE

that

will guarantee a sufficient low bit error rate. 2.2 Propagation Experimental Scenario As measurements were not available for all sites, it was not possible to draw countrywide conclusions. Nevertheless, available data obtained from one of the sites, Akure, can allow for a point-wise comparison. Propagation campaign over an Earth-space path at Ku-band frequency of 12.245 GHz has been carried out in the year 2012 at the Department of Physics Federal University of Technology, in Akure, Nigeria. The Ku-band modulated signal was received by a 90cm offset parabolic dish, at an elevation angle of 53.2o to the down converter and the beacon receiver. The down converted Ku-band signal from EUTELSAT W4/W7 was then fed to a digital satellite meter and a spectrum analyzer for signal level monitoring and logging into a computer unit. Detailed characteristics of the setup link are reported on the work of [23]. Comparison between measured attenuation, the predicted and the ITU model is presented in Figure 2. A strong correlation could be seen between the measured and the predicted value, particularly at lower time percentages. However, values obtained based on the ITU model overestimates the rain-induced attenuation in this locality. This is evident at 0.01% unavailability of time where measured attenuation was 7.1 dB while the predicted attenuation is about 8.3 dB, yielding a relative error of

Transmitted Power

Meteorological Parameters

Power

Frequency

Type Modulatio

n

Increase Power

SNR

Feedback

Atmospheric Losses

(Attenuation inclusive)

Hidden Layers

Other losses SNR


about 16%. The relative error was obtained from the dB values based on the recommendation of [24].

Figure 2: Comparison between the measured and predicted signal attenuation in 2012

3. RESULTS AND DISCUSSION

3.1 Propagation Channel Influence The methodology necessary to design the physical layer of satellite communication systems, especially at Ku-band, relies on the implementation of a propagation margin to take into account rain attenuation. The link budgets are actually closed by finding solutions to a worst case scenario. The worst case is chosen for an instance percentage of the whole coverage (between 90% and 95%) on the one hand with respect to attenuation, and on the other hand with respect to carrier-to-interference ratio. It corresponds generally to both edges of coverage and low elevation. A performance assessment for the worst case scenario over a typical location among the study locations is considered (Lagos, Nigeria) for the corresponding (i) availability (or outage time), and (ii) evaluation of the system capacity or throughput. Figures 3 (a and b) show predicted cumulative distribution function (CDF) of total impairment performed with the MM model at Ka-band downlink and uplink frequency respectively, over different elevation angles, for a typical station in Ikeja, Lagos, a semi-coastal locality of Nigeria with a worst case with respect to attenuation. In order to affirm the safety margin of a system which is a function of reliability /availability, the desire total hours of outages must be established. Conventionally, the reliability/availability of a system ranges between 99.5 % (44 hours allowed outages) to 99.9% (0.88 hours of allowed outages). In this work we adopted 99.5% of reliability. Therefore, fade margins of about 12 dB on the uplink and of 8 dB on the downlink may allow the 99.5% availability requirement to be fulfilled good QoS at Ka-band for Lagos coverage. However, for Q-band frequencies needed for a future network, at the same target availability in a conventional system design, several tens of dBs would be needed to be compensated with a static margin, which is not possible

due to technology limitations, leading to low availability only. Figure 4 shows a typical Q-band uplink frequency for total impairments for Lagos, Nigeria. The same trend could be observed in the other locations, although with different compensation margins.

(a)

(b)

Figure 3: A typical prediction of total attenuation at Ka-band (a) downlink and (b) uplink frequency for Lagos

Figure 4: A typical prediction of total attenuation at Q-band uplink

3.2 Impairments as a Function of Frequency and Rainfall Rate


Figures 5 (a, b and c) present a 3D plot of RIA at propagation angle of 55o as a function of frequency and rain rate for Port Harcourt, Lagos and Akure respectively. We observed the normal trend of the influence of the attenuation on the resultant carrier frequency. As usual, attenuation increases as the carrier frequency increases. At the lower range of rainfall rate values (i.e. stratiform rain type- 0 < R < 10 mm/h), attenuation values are very steady across all the frequency ranges considered but rise suddenly due to the contribution of the convective rain type. Thus, adopting the intelligent model will provide the designer with a perceptible view of approximate rain attenuation values that can be estimated at any desired location, for all ranges of operational frequencies, percentages for the average year probability prediction, and for any elevation angle [22]. 3.3 Influence of Rain Rate and Propagation Angle on

Propagation Impairments RIA for a wide range of rainfall rate and propagation angle at Ka band frequency is as shown in Figure 6 for a typical station, PH. The result shows that the total rain-induced attenuation that belongs to the 0 to 30 dB group is more associated with the light rain rate (i.e. drizzle and widespread rain types ( 0 < R < 10 mm/h) along the propagation angles. However, as the rain rate increases to about 30 mm/h there appears a sudden rise in total attenuation due to the contribution of the convective rainfall type. This result will assist system designers to easily approximate the total attenuation needed for link budgeting at any desired propagation angle of satellite networking. The same trend could be observed in the remaining stations, although with different values of impairments.

(a)

(b)

(c)

Figure 5: 3D plots of rain-induced attenuation as a function of frequency and rain rate for (a) PH, (b) Lagos,

and (c) Akure 3.4 Output SNR and Adjusted Output SNR Based on the IWT adopted in this study, we have also varied the feed-in parameters for the purpose of overcoming the signal degradation due to the atmospheric impairments as well as improving the SNR. Figures 7 (a, b and c) compare the outputs of SNR ranges at a typical frequency of 20 GHz before adjustment for PH, Lagos and Akure respectively, while Figures 8 (a, b and c) are for outputs SNR after adjustment for the respective stations on the same frequency and weather condition.

Figure 6: A typical rain-induced attenuation at PH


The aim is to achieve a better uplink power control (ULPC) for dynamic fade mitigation under an unpredicted weather condition. At the frequency considered, the relative transmit power ranges between -84 to ~ - 72 dB to produce SNR between ~ 170 and ~ 122 dB. However, by adopting the IWT via Fuzzy logic, the adjusted output SNR now ranges from 180 to ~ 100 dB for relative transmitted power between -46 and -32 dB, and for the same rainfall rate in both results. It must be noted that there are limits to increasing relative transmit power up to around –30 dB. Once this value has been reached, modulation/coding selection should match in order to adjust SNR as per Table 2.

(a)

(b)

(c)

Figure 7: The outputs of SNR range before adjustment for (a) PH, (b) Lagos, and (c) Akure

This kind of adjustment often results in what is termed dynamic uplink power control (ULPC). The dynamic ULPC allows the output power of a transmitting Earth station to be matched with the uplink impairments.

(a)

(b)


(c)

Figure 8: The outputs of SNR range after adjustment for (a) PH, (b) Lagos, and (c) Akure

4. CONCLUSION

This work presents an intelligent technique with the ability to predict channel attenuation due to tropical atmospheric conditions that can enable mitigation of the channel fading condition by adaptively selecting appropriate propagation parameters. At SHF and EHF bands, the relative effect of weather attenuation was so great that an efficient and dependable method for estimating RIA was considered essential for designing efficient and intelligent control systems. This paper has presented a solution to this problem. Here, we estimated the RIA for three locations of study based on the model suitable for a subtropical/tropical region and techniques that bring noticeable improvements in the SNR on satellite communication channels enroute to these regions. This was achieved by utilising RIA estimations in the decision support techniques. The ability to better estimate RIA has resulted in a significant opportunity to model communication channels such that we are able to improve SNR by better tuning of parameters like transmit power, modulation, propagation angle, frequency and transmission rate. The ultimate benefit of this work would be in realising tolerance margins for optimal performances of communication signals under severe tropical weather conditions.

5. ACKNOWLEDGEMENTS

The authors wish to acknowledge the Centre of Radio Access Network and Rural Communication (RAN-RC) Niche Area, Mangosuthu University of Technology (MUT), Umlazi, KZN, South Africa. They are also grateful to Obiyemi O.O. for the data used in the validation in this work.

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