AIAA-2003-2355

Quantitative Assessment of Technology Infusionin Communications Satellite Constellations

Olivier L. de Weck∗ and Darren D. Chang†

Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Ryutaro Suzuki‡

Communications Research Laboratory, Tokyo, Japan

Eihisa Morikawa§

Next-Generation LEO System Research Center (NeLS), Kanagawa, Japan

A methodology for quantitative assessment of new technologies is presented in thecontext of communications satellite constellations. The fundamental idea is that newtechnologies will shift the Pareto-optimal frontier when considering tradeoffs betweenperformance, lifecycle cost and capacity. The suggested process first establishes a baselineby finding a Pareto optimal set of architectures on the basis of mature, state-of-the-art technologies (technology readiness level TRL=8). Lifecycle cost, performance andcapacity of each architecture are predicted by a modular software simulation. The fidelityof the simulation is ascertained by benchmarking against existing systems such as Iridiumand Globalstar. Next, a set of potential candidate technologies is identified whose TRLis in the range of 5-7. Each of these technologies is modelled and infused individuallyinto the simulation and the effect on the Pareto front is observed, relative to the baselinecase. The next step consists of analyzing allowable pairs of technologies to predict theirjoint effect. The methodology is demonstrated for a set of four technologies applicableto Low Earth Orbit (LEO) communications satellite constellations: optical intersatellitelinks (OISL), asynchronous transfer mode (ATM), large-scale deployable reflectors (LDR)and digital beamforming (DBF). The proposed methodology is potentially helpful intechnology selection as well as in technology portfolio management.

Nomenclatureλ = operating wavelength, [m]τ = Technology selection vectorm = Number of objectivesx = Design vectorCPF = Cost per Function, [$/min]DA = Antenna Diameter, [m]J = Objective vectorPt = Transmitter Power, [W]Tc = Technology compatibility matrixTd = Technology dependence matrixΠi = Pareto front with i-th technology

1 Introduction

THE architecture of complex systems and prod-ucts is defined during conceptual design based on

∗Assistant Professor,Department of Aeronautics and Astro-nautics, Engineering Systems Division (ESD), Member.

†Research Assistant, Department of Aeronautics and Astro-nautics, Student Member

‡Senior Research Scientist, Member.§Senior Research Scientist, Member.Copyright c© 2003 by Olivier L. de Weck. Published by the

American Institute of Aeronautics and Astronautics, Inc. with per-mission.ff

a set of system requirements. In the case of constella-tions of communications satellites a number of crucialtopological decisions regarding the space and groundsegments drive the performance, lifecycle cost, capac-ity and ultimately the likelihood of market success ofsuch systems. Examples of architectural decisions inthis context include the constellation type (e.g. po-lar, Walker or Draim patterns), the use of intersatel-lite links (ISL) and the multi-access scheme such asfrequency division multiplexing (FDMA) versus codedivision multiplexing (CDMA). In view of the billiondollar class investments required, one should only com-mit to a particular architecture after careful quantita-tive evaluation of the underlying combinatorial tradespace. It has been argued before that an “optimal”architecture should be selected from a multi-objectivePareto frontier, containing only non-dominated solu-tions.1,2 Alternatively, one may argue that an archi-tecture should yield maximum utility, whereby utilityis a non-dimensional metric of goodness that reflectsstakeholder preferences.

The conceptual designer and system architect facesanother challenge. Oftentimes he or she must selectfrom a portfolio of potentially competing technologies.Some of these technologies represent alternative im-

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AIAA-2003-2355

plementations at the intra-satellite subsystem level,e.g. spin-stabilization versus three-axis-inertial atti-tude control. In other instances emerging technologies,such as radio frequency (RF) or optical inter-satellitelinks (OISL) will enable new architectures that werenot previously realizable or conceivable. Frequentlyone has to choose between a well established, proventechnology versus a set of emerging alternative tech-nologies at various stages of maturity. The field ofmanagement of technology (MOT) has attempted tofacilitate this technology selection process by meansof various frameworks. While helpful in general R&Dplanning, many of these frameworks remain too gen-eral and do not connect well with the more quanti-tative aspects of conceptual and preliminary design.There is a growing realization that new technologieswill simultaneously affect performance, cost and ca-pacity of engineering systems in a multiobjective sense.This leads naturally to the idea of quantitative tech-nology assessment by measuring the effect of candidatetechnologies on the Pareto-optimal frontier.3 This isshown conceptually in Figure 1 for a hypothetical costversus performance/capacity trade space.

*

*

*

Objective 1: Performance or Capacity

Shifted ParetoFront due to Technology

Infusion

CommunicationsSatellite

ConstellationArchitecture

Objec

tive 2

: Cos

t

* **

*

* *

***

*

* **

*

**

*

**

**

*

**

***

**

*

*

**

* ****

*

* **

Pareto frontAnchorPoint 1

AnchorPoint 2 Utopia Point

ΠiΠo

Fig. 1 Notional effect of a new technology on amultiobjective Pareto frontier of cost (J2) versusperformance or capacity (J1). The utopia point hasmaximal performance/capacity and minimal cost,but is not achievable.

In Figure 1 each asterisk represents a particu-lar communications satellite constellation architecture.Together these architectures form the trade space. Theposition of any particular architecture in this objectivespace, J = [J1 J2]T , is predicted by a multidisciplinarysimulation that maps the design vector, x to the vec-tor of objectives, J. The architectures that fall on thePareto front are of significant interest, since they arenot dominated (see below). If one were seeking theoptima for J1 and J2 alone, i.e. the highest perfor-mance/capacity or the lowest cost solution one wouldfind the anchor points 1 and 2, respectively. A hypo-

thetical solution that embodies both optima of J1 andJ2 represents the non-achievable utopia point. Thefundamental premise of this paper is that infusing anew technology or a set of new technologies into thetrade space will generally shift the position of one ormore architectures in objective space. The effect ofthis shift on the locus of the Pareto front is of specialconcern. We propose that quantitative technology as-sessment metrics can be constructed based on shiftedpositions of the Pareto front, Πi, relative to the orig-inal Pareto front, Πo, as characterized by its anchorpoints and the utopia point.

This paper briefly summarizes previous efforts forcharacterizing the impact of technologies on system-level metrics. This includes frameworks for technologyassessment and selection in the general context of com-plex engineering systems. A review of the importanceof technology selection for communication satellitesconcludes the literature search. Next, we lay out fourassociated metrics for quantifying the technology im-pact: (a) minimum utopia distance δmin, (b) averageutopia distance µ, (c) utopia point shift υ as well as(d) the number of Pareto crossings, χ. An applicationof this assessment method to constellations of com-munications satellites in Low Earth Orbit (LEO) isdeveloped in detail. The underlying tool is a base-line simulation, whose validity is verified by bench-marking against existing and planned LEO systemssuch as Iridium and Globalstar. A technology infu-sion interface (TII) is the means by which informationabout alternative technologies enters the baseline sys-tem simulation.

Four emerging communications satellite technolo-gies are assessed: Optical Intersatellite Links (OISL),Asynchronous Transfer Mode (ATM), Large Deploy-able Reflectors (LDR) and Digital Beam Forming(DBF). Each of these technologies is quantified ac-cording to its anticipated Pareto front impact. Thelast section discusses the application of this methodto technology selection and technology portfolio man-agement. Future work must include the effect of tech-nology readiness levels (TRL) on the uncertainty inPareto impact prediction as well as the extension tomore than two objectives.

2 Previous WorkIn this section we will first review previous work in

architecture evaluation for constellations of communi-cation satellites, followed by an overview of researchon technology assessment and selection, independentof our application domain. This will illustrate themissing link between architecture evaluation and tech-nology assessment.

In order to conduct a quantitative, relative com-parison of satellite communications constellations a

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number of metrics have been proposed. Kelic, Shawand Hastings proposed a “cost per function” (CPF)metric for satellite-based internet links.4 In this workfive proposed constellations were compared based ona “cost per T1 minute” metric that took into accountthe lifecycle cost of a satellite-based T1 internet linkat a data rate of 1.544 Mbps. The cost in that paper,however is not the cost to the operator, but ratherthe price of a T1-minute to the customer that willensure a 30% internal rate of return. This contribu-tion built upon earlier work by Hastings, Gumbert5

and Violet.6 Shaw extended this work, enabling themodeling of most distributed satellite systems (DSS)as information processing networks. The methodologyproposed by Shaw is called the Generalized Informa-tion Network Analysis (GINA) methodology.7 Jillaand Miller have extended this methodology to in-clude multidisciplinary design optimization (MDO),1

i.e. the use of optimization to find a subset of good ar-chitectures in a very vast design space. The frameworkof multiobjective architecture tradespace evaluationand selection was subsequently applied to constella-tions of LEO communications satellites by de Weckand Chang,2 including benchmarking against Iridiumand Globalstar. Suzuki and coworkers8,9describe a setof key technologies that are under development as en-ablers of a next-generation global multimedia mobilesatellite communications system in Low Earth orbit inJapan. These technologies include active phased arrayantennas, digital beam forming, large scale deployableantennas and optical intersatellite links.

The next question that arises naturally is how newtechnologies can potentially impact the position of ar-chitectures in objective space. Specifically, we areinterested in observing changes in the position of non-dominated architectures along the Pareto front. Thisis the main question of the present paper.

Historically, technology assessment has been the fo-cus of the Management of Technology (MOT) com-munity. Utterback has observed the evolution of tech-nologies over time and derived a model of the dynam-ics of technological and industrial evolution from it.10

Tschirky discusses technology assessment as an inte-gral function of technology and general management11

and describes it as the systematic identification andestimation of present and future impact of technologyapplication in all areas of society. Technology classifi-cation has been proposed by van Wyk.12 Gordon andStower make an explicit connection between technol-ogy forcasting and complex system simulation.13 Oneway to distinguish between incremental, architectural,modular and radical innovation has been proposed byHenderson and Clark.14 While architectural innova-tion changes only the linkages between core conceptsand components but keeps the technologies the same,

modular innovation keeps the linkages the same, butsubstitutes new technologies within the architecture.Radical innovation overturns both the architectureand technologies at the same time. The approachpresented in this paper is most applicable to modularinnovation.

More recently a systematic approach to technologyselection for infusion in complex systems has been pro-posed by Mavris, DeLaurentis and co-workers.15 Thisapproach is called Technology Identification, Evalua-tion and Selection (TIES) and is useful for sortingthrough a large set of candidate technologies to beinfused in aircraft systems. The technology selectionproblem is treated as a bi-level optimization problem.The present paper uses the impact of technologies onthe Pareto front as a measure of technology effective-ness. This differs from previous work in the sense thatthere is no attempt to develop a single scalar met-ric of technology effectiveness. It will become clearthat there are subtleties in technology infusion that arelost by converting to a single measure of effectiveness.The treatment of the Pareto frontiers, representing thebest tradeoff between system performance and capac-ity versus cost, is inspired by the s-Pareto Frontierapproach developed by Messac and Mattson.16

One crucial aspect in determining the predictiveaccuracy of technology assessment is the stage of ma-turity of technologies under consideration. We refer toNASA’s technology readiness level (TRL) scale17 toestimate the uncertainty in these predictions in futurework.

3 Quantitative Technology InfusionAssessment Methodology

Key Assumptions

The methodology proposed here assumes that it ispossible to create useful models of complex engineeringsystems such as satellite communication constellationsduring the conceptual phase of design. These mod-els capture the relationship between design inputs andsystem behavior as the outputs. The inputs are ar-chitectural design variables such as constellation typeand altitude, satellite transmitter power level, multi-access scheme and the presence of intersatellite links,among others. The outputs are the expected capacityand lifecycle cost subject to a fixed per-channel per-formance requirement. Benchmarking against existingsystems gives confidence in the validity of such models.The architectural design space is first explored and thebest tradeoff architectures (Pareto-optimal surfaces)are identified based on existing technologies.2

General Methodology

This section introduces the proposed methodologyfor quantitative technology assessment. The process

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is composed of six sequential steps and is illustratedin Figure 2.

Step 1:Baseline Trade Space Exploration

Step 2:Technology Identification,Classification and Modeling

Step 3:Technology InfusionInterface Development

Step 4:Individual Technology

Impact Assessment

Step 5:Assessment of Tech-nology Combinations

Step 6:Postprocessing andResults Interpretation

xDesignVector

BaselineParetoFrontier

Set ofCandidate

TechnologiesT

TechnologyCompatibility,Dependency

Matrices

ParetoImpactMatrix

Technology Portfolio Management

ModelsLab ResultsInterviews

ShiftedParetoFrontiers

TII

TechnologySelection

vector

01

0M

+

-

Πo

Πi

ΜΠ Σ

Start

τ=

Fig. 2 Quantitative Technology AssessmentMethod - Process Diagram

Step 1: Baseline Trade Space Exploration

The first step is the exploration of a baseline tradespace, using only mature, state-of-the-art technologies.The procedure for doing this was documented in pre-vious work.1,2 The result from this step is a set ofcorresponding objective vectors, Ji(xi) from which onefinds the Pareto set, Πo, of non-dominated architec-tures, x∗.

Fundamentally the architectural choices are cap-tured by a “design vector” x and the metrics by whichthe merits of a particular LEO system architecture areassessed are contained in the objective vector J. Otherinputs are vectors containing constant parameters, c,and requirements, r. Thus, there is a mapping fromdecision space to objective space:

x 7→ J = f (x,c,r) (1)

The set of feasible vectors X , where x ∈ X , de-fines an architectural design space. The problem isto find, which corresponding objective vectors, J =[J1, J2, ..., Jm]T , are non-dominated in objective spaceJ , where J ∈ J .

Dominance is defined by Steuer18 as follows: Let x1

and x2 be two alternative architectures represented bytheir objective vectors J1 and J2, respectively. Note

thatJ1,J2 ∈ Rm

Then J1 dominates J2 (weakly) iff

J1 > J2 and J1 6= J2 (2)

or more precisely,

J1i > J2

i ∀ i and J1i > J2

i for at least one i (3)

Architecture 1 (J1) strongly dominates architecture 2(J2) if and only if

J1i > J2

i ∀ i (4)

In other words, if two architectures provide the samedata rate, bit-error-rate and link margin per channel,but one achieves this at a higher capacity (greaternumber of channels or throughput) and lower lifecy-cle cost than the other, it would be considered to benon-dominated. The set of all non-dominated archi-tectures, x∗ ∈ X , forms what is known as the Paretofrontier in J . It is this set, Πo, of architectures thatwe are seeking during conceptual design.

Step 2: Technology Identification, Classificationand Modelling

The second step consists of identifying new tech-nologies that can potentially be infused in the systemof interest, T = [T1 T2 . . . Tt]T . The maturity ofeach technology must be assessed and NASA’s tech-nology readiness levels (1-8) form a well establishedscale. Matrices showing the relative relationships oftechnologies with respect to each other can be assem-bled.15 The technology compatibility matrix, Tc, issymmetric and has zero entries for technologies thatare compatible and a 1 for technologies that are mu-tually exclusive. An example of mutually exclusivetechnologies in spacecraft are gravity gradient stabi-lization and high-angular resolution optical payloads.

Tc =

− 1 0 0− 0 1

− 0−

(5)

In the above matrix technologies T1 and T2 cannotbe selected together, the same is true for T2 and T4,all other combinations are allowed. Finally, one tech-nology may be implemented only after the other isimplemented. This represents a technology depen-dence, as represented by the technology dependencematrix, Td.

Td =

− 0 1 00 − 0 01 0 − 01 0 1 −

(6)

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Here Technology T1 requires simultaneous implemen-tation of T3, T2 depends on no other technology, im-plementation of T3 requires T1 and T4 requires T1 andT3.

Each technology must be modelled quantitatively.There are three options for generating such a technol-ogy model:

1. Physics-based first principles

2. Data from prototype/benchtop tests

3. Empirical relationships based on expert inter-views

Step 3: Technology Infusion InterfaceDevelopment

The technology infusion interface (TII) infuses newtechnologies into the system simulation and evaluatestheir effects on intermediate variables during the sim-ulation. This is shown graphically in Figure 3.

DesignVector

TII

M1 M2 M3

T1 T2

work space

� Tt

TechnologyInfusion

Baseline System Simulation

TechnologySelectionVector

� Mmx ObjectiveVectorJ

τ

Fig. 3 Technology Infusion Interface

The key idea is that the TII is modular and doesnot require recoding of the previous system simulationevery time a new technology needs to be investigated.The outputs of the TII are the impacts of the newtechnologies on intermediate variables during the sim-ulation.

Step 4: Individual Technology Assessment

In this phase technologies can be selected individ-ually, by setting the corresponding i-th entries in thetechnology selection vector, τ , to “1” or “0”. Threepotential outcomes arise as discussed in Step 6. Ifthe Pareto front, Πi, recedes compared to the base-line case, Πo, the proposed technology shows littlepromise for the particular system under consideration.If the entire Pareto front, Πi, is moved towards theutopia point, the technology shows a high degree ofpromise and might be considered as disruptive (see Ut-terback10). The third case is the most common case,where one or more crossovers between the old and newPareto fronts occur. In this case the new technologyoffers advantages, but only in certain areas of the tradespace, for example for high capacity systems.

Step 5: Assessment of Combinations ofTechnologies

Next, combinations of technologies can be selectedto observe their joint effect. The set of allowable tech-nology combinations is established from the matricesTc and Td, respectively. This paper focuses on pairwisecombinations of new technologies. It is rare that morethan two new, unproven technologies will be deployedin any system.

Step 6: Relative Comparison and Interpretation

In order to compare the relative benefit of tech-nologies on a common scale, we define four metricsof technology impact, that are derived from the shiftin the baseline Pareto frontier, Πo relative to a per-turbed Pareto frontier, Πij . This is shown graphicallyin Figure 4. The four metrics, δmin, µ, υ and χ arecomputed in normalized objective space Ji/Ji,max.

* *J1/ J1,max

****** * *

* **

*

*

1

1

* *J1/ J1,max

****** * *

* **

*

*

1

1

* *J1/ J1,max

****** * *

* **

*

*

1

1

* *J1/ J1,max

****** * *

* **

*

*

1

1

min utopia distance

min

(a) (b)

(c) (d)

i-1

i+1

avg utopia distance

utopia point shift # of crossovers

J 2 /J

2,max

J 2 /J

2,max

J 2 /J

2,max

J 2 /J

2,max

δ µ

υ χ

Fig. 4 Pareto Impact Metrics

The first metric is the normalized, minimum dis-tance of the Pareto frontier to the utopia point deter-mined in the baseline case.

δmin = min

m∑

i=1

1 − J i (xk)

Ji,max

2

12 ∀ k = 1, 2, . . . , N

(7)The second scalar metric is the averaged distance,

µ, from the utopia point, which is found by integrating(summing) along the Pareto frontier between anchorpoints. Note that N is the number of solutions in theperturbed Pareto set.

µ =1N

N∑

k=1

m∑

i=1

1 − J i (xk)

Ji,max

2

12

(8)

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The third metric captures the shift between the base-line utopia point and the technology-perturbed utopiapoint. This is captured as the vector from the baselineutopia point to the perturbed utopia point in normal-ized objective space. This captures the ability of a newtechnology to extend the current state-of the art.

υ =

[J1,max(τ) − J1,max

J1,max· · · Jm,max(τ) − Jm,max

Jm,max

]T

(9)Another way to evaluate the technologies is to ana-lyze the geometric relation between the Pareto front ofthe design with the new technology and the baselinePareto front. When the two Pareto fronts cross eachother, a technology is surpassing the other in becomingcloser to the utopia point. When this crossing happensseveral times for two designs, it means a technologyis superior in some ranges of the design space and theother technology is superior in the other ranges. Thus,the number of crossover points is the fourth Pareto im-pact metric, χ.

4 Application to SatelliteCommunications Constellations

A LEO communications satellite simulator was cre-ated previously and has been continuously refinedsince 2001.2 The simulator can test a large number ofdesigns in a relatively short amount of time. Funda-mentally it takes in a vector of design variables, x, andpredicts system performance, capacity and cost in theform of the objective vector, J. This is shown schemat-ically in Figure 5 with the simulator as a “black box”.

SIMULATORDesign

variables

Performance Cost

Capacityx J

Fig. 5 Simulator Black Box

A more detailed block diagram of the simulator isshown in Figure 6.

It can be seen that the technology infusion inter-face (TII) is implemented in two separate moduleshere. The quality of a simulation must be assuredby benchmarking against a set of real systems. Thebenchmarking compares the real systems of Iridiumand Globalstar with the predictions from the simula-tion. The result shows that deviation of key systemparameters between the real systems and the simula-tions are typically below 20%. Although not perfect,the fidelity of the simulator satisfies the need of thesystem studies we will conduct, since we are mainly in-terested in meaningful relative predictions. Figures 7and 8 show sample benchmarking results for Iridium

and Globalstar as well as Orbcomm and Skybridge,respectively.

Number of duplex channels

020,00040,00060,00080,000

100,000120,000140,000Lifecycle cost [B$]

0.001.002.003.004.005.006.00

Iridium GlobalstarGlobalstarIridium

actual or plannedsimulated

Fig. 7 Iridium and Globalstar Benchmarking Re-sults

Satellite mass [kg]

0.0200.0400.0600.0800.0

1,000.01,200.0

Iridium Globalstar Orbcomm SkyBridgeNumber of satellites in the constellation

010203040506070

Iridium Globalstar Orbcomm SkyBridge

actual or plannedsimulated

Fig. 8 Simulator Benchmarking Results

A table of simulator benchmarking results is avail-able in Table 6. Generally the simulation predictssystem level variables well within 20 %.

Step 1: Baseline Analysis

As discussed in the previous section we first carryout a baseline analysis. We assume that only exist-ing, mature technologies are used in the system. Weset two simultaneous objectives: maximize J1 = to-tal data flow (throughput) over the system lifetimein [MB] and at the same time minimize J2= lifecyclecost in [B$]. We further assume a fixed communi-cations performance at a data rate of 4.6 [kbps] perduplex channel, a bit-error-rate of 10−3 and a linkfading margin of 16 [dB].1. An interest rate of 15 %is used to compute the net present lifecycle cost, nor-malized to fiscal year 2002 in U.S. $. For the baselinecase, that is, the system without any of the new tech-nologies, several different values are assigned to eachdesign variable to form a full factorial trade space of

1The actual data rate, BER and margin are adjusted inter-nally depending on the multiple access scheme and diversity

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System Input File

Start File

Cost Module

Satellite Network Module

Spacecraft Module

Launch Vehicle Module

Coverage/Constellation

Module

Capacity Module

(MF-CDMA)

Capacity Module

(MF-TDMA)

Market Module

Design Vector (x)Constant Vector (c)Requirement Vector (r)Policy Vector (p)

PerformanceCapacity, Cost

Technology Infusion

Interface 1

Technology Infusion

Interface 2

Objective Vector (J)

Fig. 6 Simulator Block Diagram

1728 possible designs. The values of the design vari-ables, x = [x1 ... xi ... x10]T are defined in Table 1.

i Design Variable Possible Values1 constellation type polar,walker2 orbital altitude,[km] 500,1000,15003 minimum elevation,[o] 8,104 diversity 1,2,35 satellite transmitter

power,[W] 200,400,6006 satellite transmitter

gain in edge cell,[dBi] 15,257 intersatellite link yes,no8 multiple access MF-TDMA

scheme MF-CDMA9 satellite lifetime ,[years] 5,810 on-board processing yes

Table 1 Design Variable Values, xi, for the FullFactorial Trade Space

The results for the 1728 runs are shown by plottingJ2 versus J1 in Figure 9. The figure also shows theiso-CPF lines for 0.1, 1.0, 10.0 and 100 [$/MB]. CPFis the cost per function and is the ratio of the totallifecycle cost, LCC over the total data flow (TDF ),i.e. the system throughput over the life of the systemin [MB]. As such the CPF is similar to the metricdeveloped by Kelic and Hastings,4 except that is doesnot contain a fixed rate of return.

Figure 9 shows the Pareto front (using weak dom-inance) according to Equation (2). All designs alongthis line are non-dominated. An overview of these ar-chitectures is shown at the end of the paper in Table 7- high altitude low power constellations dominate thelow capacity end, while low altitude high power con-stellations, featuring many satellites, characterize thelarge capacity end. Several interesting observationscan be made. First, even for a small throughput (ca-pacity), one has to invest on the order of B$ 2.0 in

order to launch a viable satellite constellation, whichprovides global coverage. Initially additional through-put comes at a modest increase in cost, but aboveroughly 109 [MB] the cost of satellite constellationsstarts to increase sharply. One may hypothesize thatthe baseline technologies do not allow the system tobe scaled up easily to high throughput levels such asthe ones required by the next generation LEO sys-tem proposed by NeLS and CRL. The lowest lifecyclecost is 1.85 [B$], while the largest throughput is 48.8billion [MB]. These two architectures define the an-chor points of the trade space and therefore define theutopia point in the lower right hand corner of Figure 9.These values are also used for subsequent normaliza-tion. The intermediate architecture #251 achieves thelowest cost per function (CPF) at 0.248 [$/MB], seeTable 7.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.10.20.30.40.50.60.70.80.91

TDF(i)/TDFmax

LCC

min

/LCC

(i)

Normalized Pareto Plot

1 1

1 1

0.5

0.5 0.5

0.25

0.25

0.1

0.1

utopia (1,1)

δ min =0.83

iso-CPF

Pareto FrontlowestCPF

Fig. 10 Normalized Pareto plot with iso-CPF lines

The highest data flow rate and the lowest life-cyclecost of all the designs define the utopia point. Thisutopia point will be used to normalize the runs withnew technologies infused.

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107 108 109 1010 10111

10

BASELINE: Full design space exploration with 1728 runs.

system lifetime data flow in MB (log)

life c

ycle

cost

in B

$ (lo

g)

0.25 [$/MB]

100 [$/MB] 10 [$/MB] 1 [$/MB] 0.1 [$/MB]

20

ParetoFrontier

iso-CPFlines

architectures

utopiaanchorpoint1

anchorpoint 2

Fig. 9 Baseline Case: Full factorial run of lifecycle cost [b$] versus the total data flow over system lifetime [MB].

Step2: Technology Identification

In this section a set of specific new communicationsatellite technologies are examined. These technolo-gies are currently under development at NeLS andCRL and are targeted for deployment in future broad-band LEO communication satellite constellations. Thecandidate technologies are:

• T1: Optical Inter-Satellite Links (OISL)

• T2: Asynchronous Transfer Mode (ATM)

• T3: Large Deployable Reflectors (LDR)

• T4: Digital/Analog Beam Forming (DBF)

The technologies selected for this study are mutuallycompatible. No use of one technology excludes the useof any other technology. The technology compatibilitymatrix is shown in Table 2.

T1 T2 T3 T4T1 0 0 0 0T2 0 0 0T3 0 0T4 0

Table 2 Technology Compatibility Matrix

The dependency relations of the technologies areshown in Table 3. If a cell is 1, the technology of thatcolumn depends on the technology of that row. Largedeployable reflectors (LDR) will increase the gain of

a spotbeam, decrease its beamwidth, θ, and thus re-duce the footprint size of the spotbeam on the ground.Sometimes this footprint becomes so small that userhandover between spotbeams becomes difficult. In thissituation, the implementation of digital beam forming(DBF) is required to ”anchor” the spotbeam on theground so that handover between the spotbeams ofthe same satellite is not necessary. The implementa-tion of LDR, therefore, depends on the implementationof DBF first. So the entry of “1” in the T3-column andT4-row is used to signal this dependency.

T1 T2 T3 T4T1 0 1 0 0T2 0 0 0 0T3 0 0 0 0T4 0 0 1 0

Table 3 Technology Dependency Table

A series of interviews were conducted with lead-ing developers of these technologies in Japan duringthe summer of 2002. Based on these interviews somereasonable scaling relationships were developed to es-timate the effect that implementation of the technolo-gies would have on system level variables. New tech-nologies are often in the process of being developed,ranging anywhere from conceptual design to prototypetesting (see NASA TRL scale). Their technical detailsare uncertain and yet maturing. This increases the dif-ficulty in modelling them in computer simulations. In

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addition, different from mature technologies, the infor-mation on technical details of these new technologiesis often at least partially proprietary. A technologysummary was drafted at the end of the interviews, asshown in Table 4.

Step 3: Technology Infusion Interface

To simulate the implementation of these new tech-nologies in the system, a Technology Infusion Interface(TII) was created, as illustrated in Figure 11. TheTII has two types of inputs: the technology selectionvector, τ , and relevant system-level variables. Thetechnology selection vector allows individual technolo-gies or allowable combinations of technologies to beturned ”on” or ”off”.

Inputs

Technology Selection

Relevant System Parameters

Technology selection, system compatibility, and technology dependency are considered�

DBF/ABFOISL ATMLDR

TIIOutputs, impacts on the

system by the new technologies

-Subroutines that implement technology

- Change in performance

-Change in spacecraftmass- Change in development cost

- Change in first unit cost�

τ

Fig. 11 Diagram of the technology infusion inter-face (TII).

The technology selection vector,τ , technology com-patibility, Tc, and technology dependency matrix, Td,together determine which new technologies can beimplemented. Then sub-routines representing eachtechnology are called into action. An example of asimplified technology model is given for the large de-ployable reflectors (LDR) as shown on the ETS-VIIItest satellite in Figure 12.

Fig. 12 Japanese Engineering Test Satellite ETS-VIII with Large Deployable Reflector (LDR) An-tenna

The contents of a function LDR contain the follow-ing scaling relationships, among others. The antenna

gain with LDR is computed as

GT,LDR = 10 · log10

(η

(π · DA

λ

)2)

(10)

in units of [dBi], where η is the antenna efficiency. Theadditional development cost is estimated as M$ 20.3and is included in the lifecycle cost. The first unitcost is estimated as the mean of two cost estimates.The first is based on historical data17 and scales withantenna mass, MA:

TFULDR,1 = 20 + 230 · M0.59A (11)

The second cost estimation relationship (CER) scaleswith antenna diameter according to the followingequation in [k$]:

TFULDR,2 = 2120 ·(

DA

5

)(12)

For DA = 6 [m] we obtain TFULDR = 2.45 [M$],GT ∼ 39 [dBi] for λ = 0.1875 [m] and MA = 70.7[kg]. These quantities would then be returned to themain simulation and either replace previous intermedi-ate system variables, be added to them or be appliedas a multiplicative factor, assuming that the “LDR”technology is turned “on”. The other technologies aremodelled analogously.

To smooth the interaction between TII and the sim-ulator, these impacts are simplified to the form of thechanges in performance parameters, in system param-eters, in spacecraft mass, in development cost, and infirst unit cost, etc. These changes are directly fed tothe other modules of the simulator and join the com-putations in each module. For example, OISL willincrease the ISL bandwidth from its original value toa typical value of 10 [Gbps], meanwhile reducing theoverall mass of the spacecraft if radio-frequency (RF)ISL was originally installed, or increase the spacecraftmass by slightly more than ≈ 100 kg if no ISL wasoriginally installed. Similarly, different changes in de-velopment cost and first unit cost caused by OISL willbe added depending on the original state of the ISLdesign variable.

Step 4: Individual Technology Assessment

At this point, the impact of each technology on thesystem is estimated individually. The full factorial run(1728) of each technology uses the same design variablecombination as the baseline case, see Table 1. Theresults are compared against the baseline results, andthe new Pareto frontier, Πi is judged in relation to thebaseline Pareto frontier, Πo. The effect of using LDRon the design space is shown in Figure 13.

One can see that there is a large effect of using LDRand the architectures in objective space are generally

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Table 4 Technology Overview. Column Descriptions: Tech. - technology acronym, Description - mainfeature, Satellite - example prototype implementation in space, Advantage- key benefit, R&D - estimatedadditional research and development cost in M$, TFU - theoretical first unit cost in M$, Drawbacks - keydisadvantage

Tech. Description Satellite Advantage R&D TFU DrawbacksOISL Replaces RF ISL Spot-4 Datarate 10Gbps 25 16.7 pointing accuracyATM packet/circuit switched NASA ACTS flexibility, efficiency 40 10-15 mass penaltyLDR DA up to 20 m ETS-VIII Gain ≈ 38-45 dBi 20.3 > 2.12 large stowed volumeDBF Ground fixed cells TBD higher gain 7-10 3.6 increased complexity

106 107 108 109 1010 1011 1012100

101

Full Factorial Exploration (1728 Runs) with LDR

system lifetime data flow in MB (log)

life c

ycle

cost

in B

$ (lo

g)

100 [$/MB] 10 [$/MB] 1 [$/MB] 0.1 [$/MB]

0.04 [$/MB]

100 [$/MB] 10 [$/MB] 1 [$/MB] 0.1 [$/MB]

baseline

with LDR

Fig. 13 Full factorial run of the total data flowover system life time, LDR implemented.

shifted towards higher throughput, but also higher life-cycle cost. This can be explained by the fact thatLDRs significantly increase antenna gain, GT (Eq. 10),while at the same time imposing a mass and costpenalty according to Equation (11). The relative ef-fect of LDR can be measured by applying the metricsfrom Figure 4 to the new Pareto front, see Figure 14.

0 0.5 1 1.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TDF(i)/TDFmax

LCC m

in/L

CC(i)

Normalized Pareto Plot (1728 Runs) with LDR

0.5

0.25 0.1

0.1

baselineutopiapoint

δmin=0.63

δmin=0.83

isoCPF

contours

baseline case

with LDRtechnology

Π3

Πo

Fig. 14 Normalized Pareto plot, LDR imple-mented.

The other technologies that were examined individ-

ually are OISL and ATM.

Step 5: Assessment of Combinations ofTechnologies

Three combinations of technologies were examined.They are OISL+ATM, ATM+LDR, and OISL+LDR.The effect on the Pareto frontier is summarized in Ta-ble 5. The technology dependency in Table had to betaken into account. Thus, digital beam forming (DBF)is automatically turned on whenever the time betweenhandovers drops below 30 sec in the simulation.

Step 6: Relative Comparison and Interpretation

A relative comparison between technologies is shownin Table 5.

Table 5 Pareto Impact Matrix MΠ. Legend:CPF: minimum cost per function of any architec-ture [$/min]

Case δmin µ υ(1) υ(2) χ CPFBaseline 0.83 0.98 1.0 1.0 - 0.248OISL 0.89 1.04 1.80 0.52 1 0.287ATM 0.84 1.02 2.37 0.65 5 0.167LDR 0.63 2.2 8.79 0.6 2 0.040OISL+ATM 0.84 1.19 3.00 0.40 0 0.200ATM+LDR 0.64 4.10 14.3 0.45 1 0.032LDR+OISL 0.73 3.15 9.83 0.39 1 0.058

A critical analysis of this table shows that the min-imum distance to the utopia point is decreased forall cases that use LDR, either alone or in combina-tion with other technologies. None of the technologiesdecreased the mean distance from the utopia point,µ, while all technologies lead to a shift of the utopiapoint in the direction of higher throughput and higherlifecycle cost. This confirms the effectiveness of thesetechnologies in the context of high bandwidth systemssuch as the Mobile Multimedia Next Generation LEOsystem described by Suzuki and coworkers.9 All fourtechnologies T1...T4 can be described as enabling ofhigh throughput, based on their Pareto effect. Onemay speculate that technologies designed to improveefficiency alone would reduce Pareto distance metrics,without significantly shifting the utopia point.

Based on this information designers can select fa-vorable technologies depending on the desired totalcapacity or throughput of the system. Very often

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this judgement is assisted by prediction of the mar-ket demand. A trend we have found is that these newtechnologies tend to do better (closer to the utopiapoint) in ranges of higher system capacity.

5 Conclusions and Future WorkThe methodology presented in this paper quantita-

tively assesses the effectiveness of technology infusionin the context of communications satellite constella-tions. This is useful for two particular situations:

1. Choosing between mature technologies (e.g. RFintersatellite links) versus a newly emerging tech-nology (e.g. optical intersatellite links) when im-plementing a particular system.

2. Assembling a technology portfolio and makingtechnology investment decisions. Given a fixedbudget for technology research one may find theincremental performance, capacity and cost im-pact of each technology relative to its anticipateddevelopment and deployment cost. Naturally,such predictions will be quite uncertain, but theprocess of quantitative impact assessment is use-ful in itself.

Future work needs to focus on situations where mul-tiple missions or systems are under consideration atonce. It is possible that a technology will show a sig-nificant advantage in one scenario, e.g. broadband,broadcast systems, but no advantage in a different sit-uation, e.g. personal, mobile voice communications.Also, the uncertainty in predictions such as the onesshown in Table 5 will increase with decreasing technol-ogy readiness level; this must be investigated further.The Pareto impact metrics, particularly, χ, have tobe extended to more than two objectives. Finally,it would be interesting to investigate - a-posteriori -what turned out to be disruptive technologies basedon historical data that would allow to reconstruct theevolution of the Pareto frontier over time.

6 AcknowledgmentsThis paper is a joint effort of the Massachusetts

Institute of Technology (MIT) of the United States,the Communications Research Laboratory (CRL) ofJapan as well as the Next-Generation LEO SystemResearch Center (NeLS) of Japan. The methodol-ogy, simulator and technology infusion interface weredeveloped at MIT; the new communications satellitetechnologies used as hypothetical evaluation examplesare proposed and developed jointly by CRS and NeLS.This research was funded by the Alfred P. Sloan Foun-dation under grant number 2000-10-20 with Dr. GailPesyna serving as the program monitor, Prof. Richard

de Neufville and Dr. Joel Cutcher-Gershenfeld serv-ing as project managers and Mrs. Ann Tremelling ofthe Engineering Systems Division (ESD) serving as thefiscal officer.

Table 7 Selection of Pareto-optimal architectures,Πo, in the baseline case, Πo

# h [km] ε [deg] diversity Pt [W]1444 1500 8 1 2001595 1500 10 1 2001491 1500 8 2 2001643 1500 10 2 2001212 1000 8 2 2001548 1500 8 3 2001691 1500 10 3 2001228 1000 8 2 4001371 1000 10 2 4001260 1000 8 3 2001403 1000 10 3 2001276 1000 8 3 4001419 1000 10 3 400924 500 8 2 200396 1000 8 3 200972 500 8 3 2001115 500 10 3 200988 500 8 3 40060 500 8 2 2001003 500 8 3 600204 500 10 2 200108 500 8 3 200251 500 10 3 200268 500 10 3 400284 500 10 3 600283 500 10 3 600

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