Quantitative Assessment of Technology
Infusion in Communications Satellite
Constellations
Unit 3
Olivier de Weck and Darren Chang, MIT, U.S.A.
Ryutaro Suzuki, CRL, Tokyo, Japan
Eihisa Morikawa, NeLS, Kanagawa, Japan
21st International Communications Satellite Systems
Conference, 15-19 April 2003, Yokohama, Japan
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Outline
• Introduction and Motivation
• Previous Work in Technology Assessment
• Quantitative Technology Infusion
Assessment Methodology
• Application to Satellite Communications
Constellations
• Conclusions
• Future Work
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Motivation
• The architecture of satellite communications systems
(concept) must be carefully selected
• Selection of architectures can be done quantitatively
based on performance, cost and capacity predictions –
see AIAA-2002-1866
• A number of new technologies are currently under
development for GEO and LEO Systems, e.g.
– Large Deployable Reflector (LDR) Antennas
– Optical Inter-satellite Links (OISL)
• System Designers/Architects must often choose between
competing technologies – need a quantitative method
• Generally, better understand the relationship between
architectures and technologies
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Conceptual Design Space
Design
(Input)
Vector
Simulator
Performance
Capacity
Cost
Can we quantify the conceptual system design
problem using simulation and optimization?
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Key Idea: Pareto Impact
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Previous Work
• Metrics for comsat architecture evaluation: “cost per
function” – CPF – [$/min] at a fixed data rate R, BER pb
and link margin (Hastings, Shaw….)
• Architecture Evaluation and Selection using MDO (multi-
disciplinary design optimization) – Miller, Jilla, de Weck
• Research in new generations of satellite constellations
(e.g. NeLs, R. Suzuki) and new technologies
• Technology assessment proposed by Management of
Technology (MOT): Utterback, van Wyk, Henderson and
Clark
• Technology Selection: Mavris, DeLaurentis
> Perceive a missing link between architecture
evaluation and technology selection
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Assumptions
• Possible to create “high-fidelity” simulations of
satellite communications systems during
conceptual design
• Performance per channel is fixed:
– Data Rate
– Bit-Error-Rate
– Link Fading Margin
• Tradeoff between system capacity and
lifecycle cost
• Architectures are realizable with existing,
mature technologies
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Proposed Methodology
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Steps 1-3
Step 1: Baseline Trade Space Exploration x J f x,c,r
- Obtain Baseline Pareto Frontier Po
Step 2: Technology Identification, Classification, Modeling
- Understand technology dependencies: Tc, Td
- Technology modeling : physics based, prototype
data, empirical relationships (expert interviews)
Step 3:
Technology
Infusion Interface
Development
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Steps 4-6
Step 4: Individual
Technology Assessment
1 0 0
T
Step 5: Assessment of
allowable combinations of
technologies
1 0 1
T
Step 6: Comparison and
Interpretation
- based on Pareto Impact
Metrics (4)
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Application: LEO Com Sat
LEO Constellation
50 satellites
5 planes
h=800 km
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Benchmarking
Benchmarking : validating a simulation
by comparing the predicted response against reality.
Benchmarking Result 1: Simultaneous channels of the Benchmarking Result 2: Lifecycle cost
constellation
6.00
Lifecycle cost (billion $)
140,000
simultaneous channels
5.00
of the constellation
120,000
4.00
100,000
Number of
actual or planned
80,000 actual or planned 3.00
simulated
60,000 simulated 2.00
40,000
20,000 1.00
0 0.00
1 Iridium 2 Globalstar 1 Iridium 2 Globalstar
Iridium and Globalstar Iridium and Globalstar
Benchmarking Result 3: Satellite mass Benchmarking Result 4: Number of satellites in the constellation
1,400.0 70
Number of satellites in the
Satellite mass (kg)
1,200.0 60
1,000.0
constellation
50
800.0 actual or planned
40 actual or planned
600.0 simulated
30 simulated
400.0
200.0 20
0.0 10
Iridium Globalstar Orbcomm SkyBridge
1 2 3 4 0
Iridium Globalstar Orbcomm SkyBridge
1 2 3 4
Iridium , Globalstar, Orbcom m , and
SkyBridge Iridium , Globalstars, Orbcom m , and SkyBridge
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Design Trade Space
Design Vector
1728 Full Factorial Combinatorial Design Space
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Baseline Case
Baseline
Design
Space –
Uses only
Existing,
Mature
Technologies
Channel Perf:
R=4.8 [kbps]
pb=0.001
LM=16 [dB]
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Technology Portfolio
Technology Dependency Matrix
T1 T2 T3 T4
T1: Optical Inter-Satellite Links (OISL) T1 0 1 0 0
T2: Asynchronous Transfer Mode (ATM) T2 0 0 0 0
T3: Large Deployable Reflectors (LDR)
T4: Digital/Analog Beamforming (DBF) T3 0 0 0 0
T4 0 0 1 0
Techno Description Satellite (+) (-)
OISL Replace RF ISL Spot-4 R>10Gbps Pointing
requirement
ATM Packet/circuit switching ACTS Efficiency Mass penalty
LDR DA up to 20m ETS-VIII Gain ~ 38- Large Stowage
45 dBi Volume
DBF Ground fixed cells TBD Handover Complexity
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Example: Impact of LDR
Large Deployable
Reflectors (LDR)
ETS-VIII
DA 2
GT , LDR 10 log10
TFU LDR,1 20 230 M A
0.59
DA
TFU LDR ,2 2120
5
E.g. for DA=6[m] -> GT~39 [dBi], =0.19[m], TFU=2.45 [M$]
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Pareto Impact – Example LDR
Po is the normalized
Pareto front with
baseline technologies
alone.
P3 is the normalized
Pareto front with LDR.
Decreased
utopia point
distance
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Overview Results - MP
LDR has a large effect on dmin and CPF, but caution…
benefits only come in for high capacity/throughput.
OISL in isolation shows less benefit, however the system here is
narrowband (4.8 kbps), expect benefit for broadband.
All technologies increase throughput, good for NeLS !
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Conclusions
• Presented a methodology for quantitatively assessing
technology impact on Communication Satellites
– Choose between mature, existing technologies versus newly
emerging, competing technologies
– Technology portfolio & technology investment decisions
• Use simulation to predict performance, cost and
capacity for a set of candidate architectures
– Careful benchmarking required
– Modular simulation architecture eases investigation of a large
set of technologies
• Current technologies under development for NeLS
make sense for broadband, multimedia system
• Engineering Systems Industry Study will be available
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Future Work
• “Harden” and verify this methodology by deploying in an
industrial/satellite manufacturer setting, apply to GEO
• Uncertainty in effect on Pareto front P due to technology
maturity – e.g. measured via NASA’s Technology
Readiness Levels (TRL) – probabilistic
• Expand work to more than two (2) objectives
• How to deal with “disruptive” technologies that enable new
architectures – in that case don’t have a baseline Pareto
front to compare to, e.g. introduction of ISL when only
“bent-pipe” was known.
• Understand relationship between Pareto Impact metrics
and technology obsolescence
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