Future Communications Study Technology Assessment Team: Outcome of Detailed Technology Investigations

1
Future Communications Study Technology Assessment
Team Outcome of Detailed Technology
Investigations

• Presented at ICAO ACP WGC Meeting,
• Brussels, Belgium
• September 19, 2006
• Prepared by
• ITT/Glen Dyer, Tricia Gilbert
• NASA/James Budinger

2
Briefing Outline

• Overview
• L-Band Modeling
• L-Band Channel Modeling
• L-Band Cost Modeling
• P34 Modeling
• LDL Modeling
• Interference Modeling
• SATCOM Availability Modeling
• C-Band Modeling

3
Overview

• Detailed analysis of all the short listed
  technologies against all of the evaluation
  criteria is prohibitively expensive
• In general, each technology has an area of
  concern that warrants detailed investigation
• Focus of L-Band investigations was to
• Define a channel model that could be used for
  common characterization of waveform performance
  in A/G channel
• Define a framework for specifying the
  infrastructure costs associated with an L-Band
  system
• Analyze recommended technologies (P34 and LDL)
  performance with common channel model and
  potential to interfere with incumbent users of
  the band
• Focus of Satellite Modeling was availability
• Focus of C-Band Modeling was airport surface
  performance

4
L-band Channel Modeling

• A literature search revealed that while many
  channel models exist for the terrestrial channel
  in close proximity to L-Band, there had been no
  previous activity to develop a channel model that
  characterizes the L-Band A/G channel.
• Most standardization bodies consider it best
  practice to test candidate waveform designs
  against carefully crafted channel models that are
  representative of the intended user environment
• As a consequence of these considerations, a
  simulation was developed to characterize the A/G
  channel at L-Band
• For modeling purposes, a severe channel (from a
  delay spread perspective) was considered
• Figures show the model context

5
L-Band Channel Modeling Methodology Overview

• Methodology used for generating power delay
  profiles
• A series of concentric oblate spheroids was
  generated using the Tx Rx locations as the
  focal points
• The semi-minor axis for each successive spheroid
  was increased by a fixed increment
• The contour of terrain trapped between two
  successive spheroids was used to calculate
  multipath dispersion for a particular time delay
• Each contour consisted of a set of terrain points
  that represented potential scatterers
• Ray-tracing was used to determine Specular and
  diffuse multipath

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L-Band Channel Modeling Methodology Details
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L-Band Channel Modeling Suggested Channel Model

• Specified model for a terminal area is shown in
  table
• Extension to larger distance can be found using
• where e 0.6337, st0 0.1 µs and A 6 dB

Tap Delay (µs) Power (lin) Power (dB) Fading Process Doppler Category
1 0 1 0 Ricean Jakes
2 1.6 0.0359 -14.5 Rayleigh Jakes
3 3.2 0.0451 -13.5 Rayleigh Jakes
4 4.8 0.0689 -11.6 Rayleigh Jakes
5 6.4 0.0815 -10.9 Rayleigh Jakes
6 8.0 0.0594 -12.2 Rayleigh Jakes
7 9.6 0.0766 -11.2 Rayleigh Jakes
8
L-Band Channel Modeling Predicted RMS Delay
Spreads

• tRMS 0.1 µs for average 1 km distance from
  transmitter in mountainous terrain (simulated)
• tRMS 1.4 µs for average 64 km distance from
  transmitter in mountainous terrain (simulated)
• tRMS 2.5 µs for 160 km aircraft-tower
  separation distance (extrapolated)

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L-Band Cost Modeling Process for Determining
Service Provider Cost
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L-Band Cost Modeling Rules Assumptions

• Assumptions
• L-Band system provides coverage to either the
  continental Unites States or to core Europe
• Coverage is above FL 180
• System Availability of Provision meets COCR
  requirements for Phase II En-route services (sans
  Auto-Execute)
• Cost elements considered are
• Research and Development
• System Design and Engineering
• Investment
• Facilities
• Equipment
• Operations and Maintenance
• Telecommunications
• Other costs (personnel, utilities, etc.)

11
P34 Modeling OPNET Simulation
12
P34 Modeling OPNET Results

• The figures show the response time of the P34
  simulation to the offered load for each of the
  transmitted messages
• It seems that sub-network latencies over P34
  protocols (SNDCP, LLC CP, LLC UP, MAC) meet COCR
  latency requirements
• Some startup outliers, but 95 is under 0.7
  seconds

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P34 Modeling Validation of Receiver Model

• The P34 Scaleable Adaptive Modulation (SAM)
  physical layer interface was modeled by
  developing a custom application using C code
• The transmitter was implemented as detailed in
  the specification for the 50 kHz channel using
  QPSK modulation
• The receiver implementation was tested against
  known results
• Top figure is from Annex A of TIA-902.BAAB-A
• Bottom figure shows simulation results for AWGN
  and the HT200 channel model

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P34 Modeling Investigation of Coding Gain

• From the previous results, it was unclear if
  satisfactory performance was being achieved in
  the mobile fading channel
• Needed to know what a raw BER of 310-3
  translated to after coding
• P34 SAM uses a system of concatenated Hamming
  codes. The basic scheme is shown in the top
  figure
• Simulated the rate ½ coding by concatenating two
  Hamming coders and a block interleaver
• Coding gain is shown in bottom figure
• 310-3 raw BER is approximately 10-5 coded BER

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P34 Modeling Predicted Performance

• The A/G channel was simulated using a two tap
  model
• Tap 1 was modeled as Rician, with a K-factor of
  18 dB, unity gain, Jakes Doppler Spectrum
• Tap 2 was modeled as Rayleigh, with a 4.8 ?s
  delay, -18 dB average energy, Jakes Doppler
• The mobile velocity was taken to be 0.88 mach
• COCR gives this as the maximum domestic airspeed
  based on Boeing 777 maximum speed of 0.88 mach
• P34 tuned frequency was taken to be 1024 MHz
• Maximum Doppler shift - 1022 Hz
• The predicted P34 performance is quite good for K
  factors greater than four

• Initial simulations indicate good performance can
  be achieved in the aeronautical channel
  (primarily a consequence of the strong LOS
  component of the received signal)
• These are initial results and are still being
  validated

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LDL Modeling Validation of Receiver Model

• To validate simulation, compare simulation
  results with theory
• The theoretical curve is the performance of
  binary CPFSK with coherent detection using n 5,
  and h 0.715 Proakis
• Model uses the same traceback length (n 5) and
  modulation index (h 0.715)

• Using a modulation of 0.715 minimizes probability
  of error for binary CPFSK Schonhoff 1976

17
LDL Modeling Investigation of Coding Gain

• A modulation index of 0.715 was required to
  validate the model with published results, but
  LDL calls for a modulation index of 0.6
• Changing the modulation index from 0.715 to 0.6
  pushes the BER curve out 1 dB
• The Reed-Solomon (72,62) code provides a coding
  gain of 3-4 dB in the expected region of
  operation

In order for the RS code to provide a substantial
coding gain, the raw BER must be less than 10-2
and ideally, it should be less than 210-3
18
LDL Modeling Predicted Performance

• The LDL channel model is a conservative model
  that introduces an irreducible error floor to
  system performance
• Based on the results of this model, LDL will
  require channel equalization to mitigate the
  effects of the Air/Ground Aeronautical Channel in
  L-Band

• The plot below shows the system performance of
  LDL in the presence both AWGN and the L-Band
  Channel Model

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Interference Modeling UAT Performance

• The top chart provides a collection of BER curves
  for varying degrees of LDL Interference into UAT
  signal
• The bottom chart provides a collection of BER
  curves for varying degrees of P34 Interference
  into UAT signals
• From the curves, it would appear that a C/I ratio
  between 12 and 15 dB is required for minimum
  degradation to the UAT receiver
• LDL has slightly better performance than P34 in
  terms of not interfering with UAT receivers

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Interference Modeling Mode S Performance

• Probability of correct preamble detection curves
• Based on an algorithmic assumption to declare
  preamble detection of
• 94 correlation
• 100 correlation
• Probability of false preamble detection curves

21
SATCOM Availability Modeling Overview

• Two satellite service architectures with AMS(R)S
  frequency allocations were selected for
  consideration in this availability analysis
• Inmarsat-4 SwiftBroadband service
• Iridium communication service
• Calculated availability of these architectures
  was contrasted with the calculated availability
  of a generic VHF terrestrial communication
  architecture
• Data communications architecture based on
  existing infrastructure

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SATCOM Availability Modeling Approach

• Utilized SATCOM availability analysis model
  described in RTCA DO-270
• Defines availability fault-tree to permit
  individual characterization and evaluation of
  multiple availability elements
• Organized into two major categories
• System Component Failures
• Fault-Free Rare Events
• Model is useful for comparing architectures and
  was used for this study

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SATCOM Availability Modeling Summary Results

• Summary
• Limiting factors for availability are as follows
• SATCOM systems
• Satellite equipment failures and RF link effects
• Capacity Overload (Iridium)
• Interference (Iridium)
• VHF Terrestrial communication systems
• RF link events

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C-Band Modeling 802.16e Transmitter Model
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C-Band Modeling 802.16e Receiver Model
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C-Band Modeling Model Validation
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C-Band Modeling Results
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Action Request

• The ACP Working Group is invited to consider the
  technology investigation activities described in
  this paper, and provide comments if desired
• It is recommended that the ACP Working Group
  consider the A/G channel model that is presented
  in this paper and adopt it for the evaluation of
  candidate technologies for the Future Radio
  System
• It is recommended that the ACP Working Group
  consider the cost modeling approach that is
  presented in this paper and adopt it for the
  evaluation of candidate technologies for the
  Future Radio System