Deep-Space Ka-band Link: Design, Continuity andCompleteness.

Shervin ShambayatiJet Propulsion Laboratory, California Institute of Technology

4800 Oak Grove Dr.Pasadena, CA 91109

E-mail: Shervin.Shambayati@jpl.nasa.gov

Abstract— The Mars Reconnaissance Orbiter (MRO) Ka-band Demonstration has been indefinitely postponed and itsobjectives must now be met through other means. One ofthese objectives is the evaluation of the continuity and com-pleteness performance of the deep-space Ka-band link for dif-ferent link design criteria. To meet this objective, the datafrom the Water Vapor Radiometers (WVR) and the AdvancedWater Vapor Radiometers (AWVR) at the three Deep SpaceNetwork (DSN) communication complexes were used. Alongwith these data, MRO’s DSN antenna allocation schedule,Earth-MRO geometry and telecom parameters from MROwere utilized to emulate the Ka-band link performance overa ten-month period. One pass per week per complex wasselected for a total of 129 passes (43 passes per complex).For each pass, at most two data rates were chosen such thatthe expected data return relative to the monthly atmosphericnoise temperature distribution for the given complex wouldbe maximized subject to different minimum availability re-quirements (MARs). The performance of the link was mea-sured in terms of data return, data loss, effective data rate,link availability, number of good periods, number of bad peri-ods, good and bad period duration statistics, number of passeswith outages and link stability. As expected, as the MAR wasincreased, the link availability and link stability increased.However, even with a MAR of 99%, 16 passes suffered out-ages due to weather effects. The data return remained rela-tively the same for MARs between 10% and 80% but it de-clined rapidly as the MAR approached 99%. These results in-dicate that a simple margin policy cannot guarantee data com-pleteness and retransmissions must be used. Given that someform of retransmission has to be used with Ka-band, it is alsorecommended that the link be designed with approximately80% MAR so that the data loss could be reduced substan-tially without incurring a significant penalty in data return.

TABLE OF CONTENTS

1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 MRO KA-BAND CAPABILITIES . . . . . . . . . . . . . . . . . . . 23 METHODOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 PERFORMANCE METRICS . . . . . . . . . . . . . . . . . . . . . . . . 35 RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1 1-4244-1488-1/08/$25.00 c©2008 IEEE2 IEEEAC Paper #1042, Final Version 1, Updated 8/11/2007.

6 CONCLUSIONS AND CAVEATS . . . . . . . . . . . . . . . . . . . . 8

ACKNOWLEDGMENTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

BIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1. INTRODUCTION

Because of limited (50 MHz) spectrum allocation at 8.41-GHz X-band for deep-space missions’ downlink operations,future high-rate deep-space missions will be using the 500-MHz spectrum allocation at 32-GHz Ka-band for their down-link. Since Ka-band is much more susceptible to adverseweather events than X-band, a different operations conceptwas needed. This operations concept, based on maximiz-ing the expected data return during a pass, was to have beendemonstrated with Mars Reconnaissance Orbiter (MRO) dur-ing its primary science phase (PSP) [3]. For this purpose,MRO carried a complete Ka-band telecommunications suite.

During MRO cruise phase, the functionality of this suite wasfully verified and some limited Ka-band performance datawere obtained [5][6]. However, on May 24, 2006, during aer-obraking in Mars orbit, MRO’s primary Ka-band chain failed.Although a backup was available, on January 24, 2007, it wasdecided to suspend MRO Ka-band operational demonstrationindefinitely because of possible risk to the mission. Since noKa-band tracking was performed during the PSP, the demon-stration could not take place. Therefore, attempts were madeto meet the objectives of the demonstration through othermeans.

The main objective of the demonstration was to evaluate theperformance of the deep-space Ka-band link designed ac-cording to Ka-band operations concept in terms of its data re-turn, completeness and continuity. Lacking Ka-band teleme-try from deep-space missions, it was decided to use the atmo-spheric noise temperature measurements from Water VaporRadiometers (WVRs) and Advanced Water Vapor Radiome-ters (AWVRs) at the three Deep Space Network (DSN) com-munication complexes at Goldstone, California; near Can-berra, Australia and near Madrid, Spain. In this approach,passes were selected from MRO’s DSN schedule and Earth-MRO geometry and the parameters for MRO Ka-band com-munications capabilities were used to design the link. TheWVR/AWVR data were used along with models for the gainand system noise temperature (SNT) of DSN Ka-band capa-

1

ble antennas [2] to emulate the performance of the designedKa-band link.

Using this approach, 43 passes per complex (for a total 129passes) were selected over a ten-month period from April 1,2006 through January 30, 2007, and the performance of thelink over these passes was calculated individually, on a percomplex basis and in the aggregate. The performance mea-sures include statistics on the duration and the number ofgood and bad periods, data return and data loss, link avail-ability, effective data rate, number of passes with outages andlink “stability.” This paper is a report on the results of thesecalculations. Based on these results, almost regardless of themargin carried on the Ka-band link, the link will suffer out-ages due to weather and therefore, a retransmission mecha-nism must be used to insure completeness of the data. In addi-tion, different complexes seem to have different outage char-acteristics and the nature of outages at each complex shouldbe studied further.

The paper is organized as follows: In Section 2, MRO Ka-band link capabilities are discussed. Section 3 details themethodology used in this paper. Section 4 describes the met-rics used for evaluating the link performance. The results arediscussed in Section 5. In Section 6, conclusions are reachedand caveats are discussed.

2. MRO KA-BAND CAPABILITIES

For the purpose of this study, MRO Ka-band capabilities wereselected to design the link and to emulate the link perfor-mance. MRO carries a 35-W RF Ka-band Traveling-WaveTube Amplifier (TWTA) transmitting over a 3-m parabolicantenna producing an equivalent isotropic radiated power(EIRP) of 101.3 dBm. Because the output symbol rate (thecombined encoded bit rate and the parity bit rate) is limitedto 6 megasymbols per second (Msps) and the turbo decod-ing speed on the ground is limited to 1.5 megabits per second(Mbps) different codes are used with different data rates. Forscience rates up to 1.5 Mbps (including headers but not par-ity bits) turbo codes are used. For data rates from 1.5 Mbpsto 2.61 Mbps, the standard NASA (7, 1/2) code concatenatedwith Reed-Solomon (255,223) code with interleaving depth 5(referred to as “concatenated coding”) is used. Concatenatedcoding is also used for the engineering rate of 27.8 Kbps. Fordata rates greater than 2.61 Mbps, Reed-Solomon-only cod-ing is used. Table 1 shows the information data rates (actualdata without headers) and the associated codes that are usedin this study. Note that in cases where multiple coding typescould be used for the same information data rate, the mostpowerful code (the code with the lowest required informationbit signal-to-noise ratio [Eb/N0] for the required frame errorrate) is selected.

3. METHODOLOGY

The methodology used in this paper is illustrated through anexample. For this example, we have selected the pass on day

200, year 2006 (2006-200) at DSS-26.

First the distance information is used to calculate the requiredgain-to-system-temperature ratio (G/T) for every data rate(Fig. 1).

Table 1. MRO Data Rates and Associated Channel Codes

Information Data Rate (bps) Channel Code

27846.76 Concatenated Coding331397.95 (8920, 1/6) Turbo Code497096.92 (8920, 1/6) Turbo Code662795.89 (8920, 1/6) Turbo Code745645.38 (8920, 1/2) Turbo Code994193.84 (8920, 1/3) Turbo Code1325591.78 (8920, 1/3) Turbo Code1491290.75 (8920, 1/2) Turbo Code1740422.20 Concatenated Coding2610633.31 Concatenated Coding2871696.64 Reed-Solomon-Only3480844.41 Reed-Solomon-Only5221266.61 Reed-Solomon-Only

40

45

50

55

60

65

70

10000 100000 1000000 10000000

Information Data Rate (bps)

Req

uir

ed G

/T (

dB

)

Figure 1. Required G/T for Different Data Rates, Day 2006-200

After the minimum availability requirement (MAR) is se-lected, the G/T profile associated for that MAR is calculatedfor the pass (black curve in Fig. 2 for 90% MAR) using zenithatmospheric noise temperature statistics [1] and equations forthe 34-m beam waveguide antennas system noise tempera-tures and gains [2] along with the elevation profile for thepass.

Using the G/T profile at most two data rates are selected suchthat the expected data return over the pass is maximized sub-ject to the minimum availability requirement in the manneroutlined in [3]. The required G/T for the selected data rates

2

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17 19 21 23 25 27 29

Time (hours UTC)

G/T

(dB

-K^-

1)

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0.1

0.3

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0.9

1.1

Sta

tus

(Goo

d=1,

Bad

=0)

Required G/T, 90% Actual G/T 90% G/T Status

Figure 2. Day 2006-200, DSS-26, Required G/T, Actual G/T, 90% G/T and Link Status

with 90% MAR is shown as the green curve in Fig. 2. Notethat the required G/T (the green curve) is always less than orequal to the 90% G/T in Fig. 2, indicating that the selecteddata rates meet the 90% MAR.

Once the data rates are selected, the WVR/AWVR data arethen used to emulate the actual G/T during the pass (the bluecurve in Fig. 2). It should be noted that the WVR/AWVRdata are sampled at approximately once every five minutes.In order to achieve better temporal resolutions, the link G/Twas emulated once every minute using linear interpolationsbetween two WVR/AWVR data points.

After the actual G/T is calculated the status profile of the linkover the pass is determined. The status of the link is “good”when the actual G/T (the blue curve in Fig. 2) is greater thanor equal to the required G/T (the green curve in Fig. 2) and is“bad” when the actual G/T is less than the required G/T. Forthe pass on day 2006-200, the status of the link is indicatedby the red curve in Fig 2.

From the link status, duration of “good” and “bad” periods aswell as data return and data loss is determined for the pass. Bylooking at the ensemble of passes, overall outage and avail-ability statistics as well as data return and data loss for thelink design approach taken are evaluated. By selecting dif-ferent MARs, the tradeoff between continuity, completenessand data return is performed. In the next section a brief de-scription of metrics used for evaluating the performance ofthe link is given.

4. PERFORMANCE METRICS

There are several measures of performance by which the linkis evaluated. The following is a brief explanation of thesemetrics and why they are important:

1. Data return and data loss for individual complexes andoverall: the primary measures of performance of the link aredata return and data loss. Data return is defined as the totalamount of data received correctly by ground stations. Dataloss is definied as the total amount of data transmitted by thespacecraft but not received correctly. A scheme that returnsthe maximum amount of data with tolerable loss of data ispreferred for science missions. In addition, these two mea-sures allow us to perform a tradeoff between different MARvalues.2. Effective data rate: this quantity is obtained by dividingthe data return by total track time and could be calculatedper pass, per complex and overall. This measure is especiallyuseful in comparing the performance between complexes be-cause the geometry of the link and the pass schedule coulddivide the total tracking time unequally among the complexesthus skewing the data return results.3. Number of good and bad periods: this metric is impor-tant since many retransmission schemes are more concernedwith the number of retransmission requests as opposed to theamount of data retransmitted. Also, in some cases, the out-ages cause the receiver to go out of lock and the receiver reac-quisition times (not taken into account in this study) lead toadditional data loss.4. Number of passes with outages: along with the number ofgood periods and bad periods, this metric provides informa-tion about how good periods and bad periods are distributedamong passes.5. Good period and Bad period statistics: this information inthe form of average duration and standard deviation is helpfulin understanding the nature of outages and good periods.6. Availability: this quantity is the ratio of the time that thelink is in the good state to the total track time. This is thestandard measure to which links are designed.7. “Stability”: this is an extension of the idea of availability.Mathematically, stability is defined as:

3

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3500

0 20 40 60 80 100

Minimum Availability Requirement (%)

Dat

aV

olu

me

(Gb

its)

Actual Data Return, Goldstone

Expected Data Return, Goldstone

Actual Data Return, Canberra

Expected Data Return, Canberra

Actual Data Return, Madrid

Expected Data Return, Madrid

Figure 3. Expected and Actual Data Return per Complex

Ψ (τ ) =

Ng∑i=1

t(g)i Iτ

(t(g)i

)

ttot(1)

where ttot is the total track time; Ng is the number of good

periods observed during that time; t(g)i is the duration of the

ith good period and Iτ (·) is an indicator function given by

Iτ (t) =

1 t ≥ τ

0 otherwise(2)

By this definition, Ψ (τ ) is the fraction of time that the linkis in a good state of duration greater than or equal to τ . Thisconcept was first introduced in [4]. Availability is Ψ (0).

5. RESULTS

The results are analyzed based on the metrics presented in theprevious section. These are evaluated in terms of variationsin MAR. In addition, some of the results are expanded uponby using specific passes and fixed MARs as examples.

Fig. 3 shows the expected and the actual data return as func-tions of MAR. Similarly, Fig. 4 shows the expected and theactual data loss as functions of MAR. Note that because thelink design method is based on maximizing the expected datareturn, the expected data return and data loss numbers arereadily available. As seen from these figures, The data returnfor Goldstone and Canberra is greater than expected whilefor Madrid it is than expected. Conversely, the data lossesfor Madrid are uniformly much greater than expected and thedata losses for Goldstone and Canberra are lower than ex-pected. This indicates that Madrid had worse than nominalweather and Goldstone and Canberra had better than nominalweather over the analysis period. However, when the data re-turn for all the complexes is looked at together, the expected

data return and the actual data return are nearly the same.Similarly, the actual losses are only slightly higher than ex-pected (Fig. 5). Whether or not this indicates that Madridweather is anti-correlated with the weather at Goldstone andCanberra requires further analysis. However, this clearly in-dicates that in this instance, if a retransmission scheme andlarge spacecraft storage were used, the link would have re-turned roughly the same amount of data as expected.

For all complexes the data return is relatively the same forMARs up to 80%. Beyond 80% the data return rapidly dropsoff. In terms of data loss, however, there is a noticeable re-duction in loss for MARs greater than 50%. This suggeststhat a MAR of 80% a good design point in that it has the leastamount of data loss while providing nearly maximum datareturn.

Goldstone has the highest effective data rate for the sameMAR and Madrid the lowest (Fig. 6). Goldstone and Can-berra have higher effective data rates than expected whileMadrid has a lower effective data rate than expected, again in-dicating that the weather at Goldstone and Canberra was bet-ter than expected and the weather at Madrid was worse thanexpected. Also note that even though the data return startsfalling off for MARs greater than 80%, the effective data ratedoes not fall off until the MAR is greater than 90%. This isdue to fact that at higher MAR values, the link design algo-rithm reduces the usable track time during a pass and thus,reduces the data return. If more than two data rates were al-lowed during a pass, the results for data return and effectivedata rate could have been different.

As expected, in general, the number of good periods and thenumber of bad periods decrease as the MAR increases (Fig.7). The only exception to this is Canberra with 99% MAR. Inthis case there is an increase in the number of good periods

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Minimum Availability Requirement (%)

Dat

aL

oss

(Gb

its)

Actual Data Loss, Goldstone

Expected Data Loss, Goldstone

Actual Data Loss, Canberra

Expected Data Loss, Canberra

Actual Data Loss, Madrid

Expected Data Loss, Madrid

Figure 4. Expected and Actual Data Loss per Complex

0

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9000

0 20 40 60 80 100 120

Minimum Availability Requirement (%)

Dat

aV

olu

me

(Gb

its)

Expected Data Return, Aggregate

Actual Data Return, Aggregate

Expected Data Loss, Aggregate

Actual Data Loss, Aggregate

Figure 5. Expected and Actual Data Return and Data Loss, Aggregate

and in the number of bad periods. This is because with theincreased MAR, one pass that was a complete loss with allother MAR values has several good periods with 99% MAR.

The total number of passes with outages decreases as theMAR increases for all three complexes (Fig. 8). Madrid hasby far the most number of outages and the most number ofpasses with outages again indicating that Madrid had expe-rienced a different weather pattern than either Canberra orGoldstone.

Note that even with a MAR of 99%, there are still a number ofpasses with outages–five passes for Goldstone, two passes forCanberra and nine passes for Madrid– constituting more than12% of the total number of passes. Fig. 9 provides insightinto why this is the case. As seen from this figure, the ac-

tual G/T is less than the required G/T for 99% MAR by morethan 8 dB between 1100 and 1200 UTC. Such large dropsin G/T occur more often at Ka-band than at X-band becausethe drops in G/T for Ka-band due to weather are roughly fourtimes greater in dB than those for X-band [5]. Therefore, forKa-band, a simple margin policy cannot be employed to in-sure completeness of the data and automated retransmissionschemes have to be used.1 Furthermore, the spacecraft mustbe designed such that enough storage is available to accom-modate these retransmissions.

Figs. 10 through 12 illustrate the average and the standard de-viation of good and bad period durations for Goldstone, Can-

1Long erasure correcting codes may also be useful. However, their opera-tional requirements both on the spacecraft and on the ground and their capa-bilities have not been fully investigated.

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Minimum Availability Requirement

Dat

aR

ate

(Mbp

s)

Expected Data Rate, Goldstone

Effective Data Rate , Goldstone

Expected Data Rate, Canberra

Effective Data Rate, Canberra

Expected Data Return, Madrid

Effective Data Rate, Madrid

Figure 6. Actual and Expected Effective Data Rate

0

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Minimum Availability Requirement (%)

Nu

mb

ero

fP

erio

ds

Number of Good Periods, Goldstone

Number of Bad Periods, Goldstone

Number of Good Periods, Canberra

Number of Bad Periods, Canberra

Number of Good Periods, Madrid

Number of Bad Periods, Madrid

Figure 7. Number of Good and Bad Periods

berra and Madrid, respectively. Madrid had poorer weatherthan either Goldstone or Canberra during the time period un-der consideration. Therefore, Madrid has shorter good pe-riod averages than either Goldstone or Canberra for the wholerange of MARs because of more weather fluctuations. Gold-stone, having better weather than the average, has the short-est bad period average for all MAR values. Canberra, hav-ing much better than expected weather, has the largest goodperiod average for all MAR values except 99%. Note thatthe good period average generally increases as the MAR in-creases except for 99% MAR for Canberra. This decrease iscaused by a single pass which was a complete loss at lowerMAR values but had several good periods for 99% MAR.

The standard deviation of good periods is nearly as large asthe average duration of the good periods. This is explained by

the fact that during passes with outages, the pass consists ofseveral good and bad periods that are relatively short, whilethe good periods constitute the entire pass for those trackswith no outages. This produces a wide range of values forgood periods, therefore the standard deviation for the durationof good periods becomes rather large.

The bad period average for all complexes is relatively smallregardless of MAR, not exceeding one hour and fifteen min-utes (Canberra, 90% MAR). The standard deviation of thebad periods is slightly larger than their average duration. Thisindicates that while the bad periods are mostly of short dura-tion, there are several of them that are quite long.

Note that good period and bad period statistics among thedifferent complex are different from each other. Whether or

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Minimum Availability Requirement (%)

Nu

mb

ero

fP

asse

sGoldstone

Canberra

Madrid

Figure 8. Number of Passes with Bad Periods

45

47

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Time (hours UTC)

G/T

(d

B-K

^-1)

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Ele

vati

on

(d

eg)

Required G/T, 99%

Required G/T, 95%

Required G/T, 90%

Required G/T, 10%

Actual G/T

90% G/T

Elevation

Figure 9. Pass on Day 2006-229, DSS-55, Required G/T values for different MARs, 90% G/T, Actual G/T and Elevation.

not this indicates different weather patterns for different com-plexes requires further analysis over a longer time period.

Fig. 13 illustrates the expected and the actual availability foreach of the complexes as well as for the three complexes ag-gregated. As seen from this figure, the aggregate expectedand actual availabilities for different MAR values match eachother very closely. However, this is not the case when theavailability is looked on a per complex basis. At lower MARvalues Madrid’s actual availability is significantly less thanthe expected availability (by more than 7%). This indicatesthat Madrid’s weather was substantially worse than expected.By contrast the weather at Canberra was much better than ex-pected since the actual availability at lower MARs is greaterthan expected by approximately 5%. The weather at Gold-stone was also better than expected. However since the ex-

pected availability for Goldstone is already very high, the im-provement is not as substantial as that for Canberra. Note thatthe aggregate availability of the link over the three complexesis slightly less than expected. This matches the results in Fig.5.

Figs. 14 through 16 display the 30-minute, the one-hour andthe three-hour stability of the link for different MAR valuesat Goldstone, Canberra and Madrid, respectively. As seenfrom these figures, the link is most stable at Goldstone withthe three-hour stability being greater than 0.9 (i.e., the linkis in a good period of three hours or longer for at least 90%of the time) even for the 10% MAR. The link at Canberra isalso relatively stable with the three-hour stability exceeding0.9 for MAR values greater than 80%. The link at Madridis far less stable than either Goldstone or Canberra with the

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Minimum Availability Requirement (%)

Du

rati

on

(ho

urs

) Avg. Good Period

Sd. Good Period

Avg. Bad Period

Sd. Bad Period

Figure 10. Average and Standard Deviation of Good Periods and Bad Periods, Goldstone

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Minimum Availability Requirement (%)

Du

rati

on

(ho

urs

) Avg. Good Period

Sd. Good Period

Avg. Bad Period

Sd. Bad Period

Figure 11. Average and Standard Deviation of Good Periods and Bad Periods, Canberra

three-hour stability exceeding 0.9 only for 99% MAR and the30-minute and the one-hour stability exceeding 0.9 for MARvalues of 90% and above. While part of this could be at-tributed to worse than expected weather at Madrid, it couldalso be due to differences among the weather patterns at thethree complexes. However, because the period analyzed inthis paper is relatively short, no such claims could be madewith any degree of certainty, and further analysis is required.

6. CONCLUSIONS AND CAVEATS

Conclusions

In this paper a method for determining the tradeoff betweendata return, continuity and completeness of the deep-spaceKa-band link using atmospheric noise temperature data fromWater Vapor Radiometer (WVR) and Advanced Water Va-

por Radiometer (AWVR) was introduced. In this method,the Ka-band link was designed using at most two data ratesthat maximized the average data return subject to a minimumavailability requirement (MAR) and WVR/AWVR data wereused to evaluate outages and data return over the link. MarsReconnaissance Orbiter (MRO) Ka-band telecommunicationcapabilities along with its geometry relative to Earth and itsschedule over a ten-month period were used to illustrate thismethod of analysis. This demonstrated that as the MAR in-creased, the total number of outage periods and the numberof passes with outages tended to decrease. However, with aMAR of 99% there were still more than 12% of passes thathad some outages. This indicated that it is necessary to use aretransmission scheme for deep-space missions with Ka-bandas the margin cost of reliability is prohibitive.

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Minimum Availability Requirement (%)

Du

rati

on

(ho

urs

)

Avg. Good Period

Sd. Good Period

Avg. Bad Period

Sd. Bad Period

Figure 12. Average and Standard Deviation of Good Periods and Bad Periods, Madrid

0.8

0.82

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Minimum Availabilty Requirement (%)

Ove

rall

Ava

ilab

ility

Actual Aggregate Availability

Expected Aggregate Availability

Actual Availability, Goldstone

Expected Availability, Goldstone

Actual Availability, Canberra

Expected Availability, Canberra

Actual Availability, Madrid

Expected Availability, Madrid

Figure 13. Expected and Actual Availability

During the period under study, significant differences in theperformance of the link among the three DSN complexeswere observed. Relative to the expected performance, the Ka-band link performed better at Goldstone and Canberra andsignificantly worse at Madrid. However, the overall data re-turn, the overall data loss and the link availability matchedexpectations very closely when the passes at all three com-plexes were considered together. The data return remainedrelatively the same for MARs up to 80% and then decreasedrapidly as the MAR increased. The data losses started to de-crease significantly for MARs above 50%. Since retransmis-sions are required even for a MAR of 99%, it is recommendedthat the Ka-band links be designed with a MAR value of ap-proximately 80% in order to reduce data losses significantlywithout incurring a significant data return penalty. It is alsorecommended that retransmission schemes be used to assure

completeness of the data.

The average duration of outages or bad periods for all com-plexes and for all MAR values was less than one hour andfifteen minutes, with Goldstone having the shortest averageduration for all MAR values. The standard deviation of thebad periods, however, was about the same as the average du-ration. This indicated that some outage periods were quitelong.

The good periods of long duration formed a substantial frac-tion of the tracking time for Goldstone and Canberra withgood periods of three hours or longer constituting more than90% of the total track time for Goldstone for all MARs andfor Canberra for MARs greater than 80%. For Madrid, how-ever, the link was not as stable with good periods of longer

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Minimum Availability Requirement (%)

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bilit

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Figure 14. 30-minute Link Stability

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Minimum Availability Requirement (%)

Sta

bilit

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Goldstone Canberra Madrid

Figure 15. One-hour Link Stability

than three hours constituting less than 90% of the total tracktime for all MARs less than 99%. This could indicate that theweather patterns at Madrid are inherently different from thoseat Canberra and Goldstone. However, this study considered arelatively short period of time and such a statement cannot bemade with certainty.

Caveats

There are two important caveats about this study. First ofall, the time resolution of the WVR and AWVR is low atroughly five minutes per sample. Therefore, these data donot capture the subsecond variations that may affect individ-ual telemetry frames at high data rates. However, since MROKa-band demonstration has been suspended indefinitely, the

0.6

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0.95

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Minimum Availability Requirement (%)

Sta

bilit

y

Goldstone Canberra Madrid

Figure 16. Three-hour Link Stability

WVR/AWVR data currently provide the best informationavailable on the effects of the weather on the Ka-band link

The second caveat concerns the duration of this study. Onlyten months of data with one pass per complex per week havebeen considered here. These data are not enough to makegeneral statements about the performance of the link. Thisstudy needs to be expanded to between two and five yearsbefore it could lead to any concrete conclusions. Having saidthis, the results of this analysis tend to validate the Ka-bandlink design approach outlined here and previously [3].

ACKNOWLEDGMENTS

This work was performed at the Jet Propulsion Laboratory,California Institute of Technology under a contract with Na-tional Aeronautics and Space Adminstration. The authorwould like to thank Steve Keihm of JPL for providing himwith WVR/AWVR data. The author would also like tothank JPL Interplanetary Network Directorate and YuhsyenShen, Wallace Tai, David Morabito, Fabrizio Pollara and JonHamkins of JPL for supporting this study.

REFERENCES

[1] Sniffin, R. W., Ed., DSMS TelecommunicationsLink Design Handbook (810-005, Rev. E), Mod-ule 105, Rev. B: Atmospheric and EnvironmentalEffects, http://deepspace.jpl.nasa.gov/dsndocs/810-005/105/105B.pdf, Jet Propulsion Laboratory,Pasadena, CA, May 26, 2006.

[2] Sniffin, R. W., Ed., DSMS Telecommunications LinkDesign Handbook (810-005, Rev. E), Module 104,Rev. B: 34-m BWG Stations Telecommunications

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Interfaces, http://deepspace.jpl.nasa.gov/dsndocs/810-005/104/104B.pdf, Jet Propulsion Laboratory,Pasadena, CA, August 1, 2005.

[3] Davarian, F., Shambayati, S., Slobin, S., “Deep SpaceKa-Band Link Management and the MRO Demonstra-tion: Past Statistics versus Forecasting,” Proceedings ofIEEE, Vol. 92 No. 12, pp. 1877-1894, December 2004.

[4] Shambayati, S., “Weather Related Continuity and Com-pleteness on Deep Space Ka-band Links: Statistics andForecasting,” IEEE Aerospace Conference 2006, pp. 1-8, Big Sky, Montana, March 5-11, 2006.

[5] Shambayati, S., Border, J. S., Morabito, D. D. and Men-doza, R, “MRO Ka-band Demonstration: Cruise PhaseLessons Learned,” IEEE Aerospace Conference 2007,pp.1-17, Big Sky, Montana, March 4-10, 2007.

[6] Shambayati, S., Morabito, D. D., Border, J. S., Davar-ian, F., Lee, D. K., Mendoza, R., Britcliffe, M. andWeinreb, S., “Mars Reconnaissance Orbiter Ka-band(32 GHz) Demonstration: Cruise Phase Operations,”AIAA SpaceOps Conference 2006, pp. 1-26, Rome,Italy, June 19-23, 2006.

BIOGRAPHY

Shervin Shambayati obtained hisBachelors of Science degree in Ap-plied Mathematics and Engineering in1989 from California State University,Northridge. Subsequently, he obtainedhis MSEE, Engineer’s Degree and Ph.D.from University of California, Los An-geles in 1991, 1993 and 2002, respec-

tively. In 1993, Dr. Shambayati joined the Deep SpaceCommunications Systems Group at Jet Propulsion Labora-tory where he took part in development and testing of DeepSpace Network’s Galileo Telemetry receiver (DGT). In 1997,Dr. Shambayati joined the Information Processing Group atJPL where he has been working ever since. With that groupDr. Shambayati has been involved in various projects includ-ing Mars Global Surveyor’s Ka-Band Link Experiment II,Deep Space 1 Ka-band testing, 70m antenna Ka-band Taskand the Mars Reconnaissance Orbiter Ka-band Demonstra-tion for which he was the Principal Invsetigator. His cur-rent research interests and activities include evaluating the ef-fects of weather outages on the spacecraft resources, Ka-bandweather forecasting, Ka-band link design and end-to-end sys-tem architecture studies for implementation of Ka-band ser-vices in NASA’s Deep Space Network.

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