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Transcript of A I R T R A F F I C O R G A N I Z A T I O N Future Communications Study Technology Assessment Team:...
A I R T R A F F I C O R G A N I Z A T I O N
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
39°30’ N
38°45’ N
10
7°3
0’
W
10
6°3
0’
W
+RCAG
39°30’ N
38°45’ N
10
7°3
0’
W
10
6°3
0’
W
39°30’ N
38°45’ N
10
7°3
0’
W
10
6°3
0’
W
+RCAG
Rx
dA1
dA2
σ01
σ02
rTS1
rTS2
rSR1
rSR2
rTR
Mountain 1
Mountain 2
Tx
Mountain k
σ0k
dAk
rSRk
rTSk
Rx
dA1
dA2
σ01
σ02
rTS1
rTS2
rSR1
rSR2
rTR
Mountain 1
Mountain 2
Tx
Mountain k
σ0k
dAk
rSRk
rTSk
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
6
L-Band Channel Modeling Methodology Details
7
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 = 0.6337, στ0= 0.1 μs and = 6 dB
Tap # Delay (µs) Power (lin) Power (dB)Fading
ProcessDoppler
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
Ad
0
8
L-Band Channel Modeling – Predicted RMS Delay Spreads
• RMS = 0.1 μs for average 1 km distance from transmitter in mountainous terrain (simulated)
• RMS = 1.4 μs for average 64 km distance from transmitter in mountainous terrain (simulated)
• RMS = 2.5 μs for 160 km aircraft-tower separation distance (extrapolated)
9
L-Band Cost Modeling – Process for Determining Service Provider Cost
No
Yes
Meetsrequirements?
Develop radio site
configuration
Determine the availability
Specify radio sitearchitecture
Develop link budget
Infer communication
distance
Derive required radio sites
for US coverage
Derive number of required radio sites
Deriverequired equipment
per radio site
Other costs (e.g cost of telco)
Derive Deployment Costs
L-Band Cost Estimating Process
No
Yes
Meetsrequirements?
Develop radio site
configuration
Determine the availability
Specify radio sitearchitecture
Develop link budget
Infer communication
distance
Derive required radio sites
for US coverage
Derive number of required radio sites
Deriverequired equipment
per radio site
Other costs (e.g cost of telco)
Derive Deployment Costs
L-Band Cost Estimating Process
10
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
The custom OPNET development
included modeling of the P34 PHY, MAC, LLC and SN Layers.
Configuration that was simulated was the fixed-network
equipment (FNE) to mobile radio (MR). The MR to MR and
repeater modes were not simulated.
The modeled configuration aligns
with the P34 “concept of use”.
The custom OPNET development
included modeling of the P34 PHY, MAC, LLC and SN Layers.
Configuration that was simulated was the fixed-network
equipment (FNE) to mobile radio (MR). The MR to MR and
repeater modes were not simulated.
The modeled configuration aligns
with the P34 “concept of use”.
The custom OPNET development
included modeling of the P34 PHY, MAC, LLC and SN Layers.
Configuration that was simulated was the fixed-network
equipment (FNE) to mobile radio (MR). The MR to MR and
repeater modes were not simulated.
The modeled configuration aligns
with the P34 “concept of use”.
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
Note outliers
13
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
• 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
QPSK BER
0.001
0.01
0.1
1
10
100
0 5 10 15 20 25 30 35 40 45 50
Es/No (dB)
BE
R (
%)
HT200
AWGN
14
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 3*10-3 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
– 3*10-3 raw BER is approximately 10-5 coded BER
• 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 3*10-3 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
– 3*10-3 raw BER is approximately 10-5 coded BER
15
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
• 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
16
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)
• 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]
Theory vs. Simulation
0.00001
0.0001
0.001
0.01
0.1
1
0 2 4 6 8 10 12 14 16
SNR (dB)
BE
R
Theory Simulation
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
• 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
BER for h=0.6 & RS Coding
0.000001
0.00001
0.0001
0.001
0.01
0.1
1
0 2 4 6 8 10 12 14 16
SNR (dB)
BE
R
Sim (h=0.6) Sim w/RS (h=0.6)
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 2*10-3
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 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
Non-Coherent (Limiter/Discriminator) CPFSK
0.00001
0.0001
0.001
0.01
0.1
1
0 2 4 6 8 10 12 14 16 18 20
SNR (dB)
BE
R
Theory (Coherent)
DISC-LIM/AWGN
DISC-LIM/AWGN/Rayleigh
DISC-LIM/AWGN/L-Band Channel
19
• 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
• 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
Interference Modeling UAT Performance
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
10 11 12 13 14 15 16 17 18 19 20
Eb/No, dB
Pro
ba
bil
ty o
f B
ER
UAT without Interference
C/I = 5 dB
C/I = 8 dB
C/I = 10 dB
C/I = 12 dB
C/I = 15 dB
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
10 11 12 13 14 15 16 17 18 19 20
Eb/No, dB
Pro
ba
bil
ty o
f B
ER
UAT without Interference
C/I = 5 dB
C/I = 8 dB
C/I = 10 dB
C/I = 12 dB
C/I = 15 dB
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
10 11 12 13 14 15 16 17 18 19 20
Eb/No, dB
Pro
ba
bilty
of
BE
R
UAT without Interference
C/I = 7 dB
C/I = 8 dB
C/I = 10 dB
C/I = 12 dB
C/I = 15 dB
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
10 11 12 13 14 15 16 17 18 19 20
Eb/No, dB
Pro
ba
bilty
of
BE
R
UAT without Interference
C/I = 7 dB
C/I = 8 dB
C/I = 10 dB
C/I = 12 dB
C/I = 15 dB
20
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
• 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
0.8
0.85
0.9
0.95
1
1.05
5 6 7 8 9 10 11 12 13 14 15
C/I, dB
Pro
bab
ilty
of
Co
rrect
Pre
am
ble
Dete
cti
on
P34 Interferer (C/No = 73 dB)
LDL Interferer (C/No = 73 dB)
Note:Conversion from C/No to C/N within necessary bandwidth can be done as follows:C/N within necessary bandwidth = C/No+10log10(2)-10*log10(4000000)
0.8
0.85
0.9
0.95
1
1.05
5 6 7 8 9 10 11 12 13 14 15
C/I, dB
Pro
bab
ilty
of
Co
rrect
Pre
am
ble
Dete
cti
on
P34 Interferer (C/No = 77 dB)
LDL Interferer (C/No = 77 dB)
Note:Conversion from C/No to C/N within necessary bandwidth can be done as follows:C/N within necessary bandwidth = C/No+10log10(2)-10*log10(4000000)
0.0001
0.001
0.01
0.1
1
5 6 7 8 9 10 11 12 13 14 15
C/I, dB
Pro
bab
ilty
of
Fals
e P
ream
ble
Dete
cti
on
P34 Interferer (C/No = 73 dB)
LDL Interferer (C/No = 73 dB)
Note:Conversion from C/No to C/N within necessary bandwidth can be done as follows:C/N within necessary bandwidth = C/No+10log10(2)-10*log10(4000000)
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
22
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
Communications Unavailable for >TOD
System Component Failures
Fault-Free Rare Events
OR
Ground Station Equipment
Failure Event
Satellite Control
Equipment Failure Event
Satellite Failure Event
Aircraft Station
Failure Event
OR
Capacity Overlaod
Event
RF Link Event
Interference Event
Scintillation Event
Communications Unavailable for >TOD
System Component Failures
Fault-Free Rare Events
OR
Ground Station Equipment
Failure Event
Satellite Control
Equipment Failure Event
Satellite Failure Event
Aircraft Station
Failure Event
OR
Capacity Overlaod
Event
RF Link Event
Interference Event
Scintillation Event
23
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
System Component Failures Fault-Free Rare Events Ground Station
Control Station
Aircraft Station
Satellite RF Link
Capacity Overload
Interference Scintillation
Inmarsat ~ 1 ~ 1 ~ 1 0.9999 0.95 ~ 1 ~ 1 ~ 1 Iridium 0.99997 ~ 1 ~ 1 0.99 0.995 - 1 0.996 ~ 1 VHF Terrestrial
0.99999
N/A ~ 1 N/A 0.999
- 2 ~ 1 N/A
Notes: 1. Iridium Capacity Overload availability of AES to SATCOM traffic is essentially one (1) (for both ATS
only and ATS & AOC). No steady-state can be achieved for SATCOM to AES traffic. 2. Terrestrial Capacity Overload availability is for VHF-Band reference architecture business case; for L-
Band Terrestrial Capacity Overload availability would be essentially one (1).
24
C-Band Modeling – 802.16e Transmitter Model
These blocks model the data randomization process
Reed Solomon Coding
Modulator
Zero Pad to Code Word Size
Zero Pad
TxSignal
Subcarrier Mapping(as shown on p. 444
of specification)
Full_BW_TestVector
Read in Data from MATLAB WS
RS Encoder
RS Encode
U U(E)
Puncture Code
Puncture
Puncture
PN SequenceGenerator
PN SequenceGenerator
Model InfoCreated by: Glen DyerCreated date: Sun Mar 19 14:11:35 2006Modified by: dye27622Modified date: Sat Jun 10 15:38:27 2006Model Version Number: 1.6
GeneralQAM
General QAMModulator
GeneralBlock
Interleaver
General BlockInterleaver
DOC
Text
XOR
DataRandomizer
Create OFDMSymbols
Create OFDMSymbols
ConvolutionalEncoder
Convolutional Coding
Integer to BitConverter
Convert Integersto Bits
Integer to BitConverter
Convert Bytes to Bits
Bit to IntegerConverter
Convert Bits to Bytes
Bit to IntegerConverter
Bit to IntegerConverter
• This is the developed model for the 802.16 OFDM Transmitter• The 802.16 standard defines the following elements for OFDM transmitter implementation
• Bit Scrambling• Concatenated Punctured Reed-Solomon and Punctured Convolutional Encoding• Bit Interleaving• Adaptive Modulation • OFDM Symbol Creation
• This is the developed model for the 802.16 OFDM Transmitter• The 802.16 standard defines the following elements for OFDM transmitter implementation
• Bit Scrambling• Concatenated Punctured Reed-Solomon and Punctured Convolutional Encoding• Bit Interleaving• Adaptive Modulation • OFDM Symbol Creation
25
C-Band Modeling – 802.16e Receiver Model
These blocks invert the data randomization process
Reed Solomon De-coding
IEEE 802.16 OFDM16-QAM Modulation
Rate 1/2 Concatenated Coding
Zero Pad1
XOR
PN SequenceGenerator
Un-do Random-ization
Insert Zero
Un-do ConvolutionalPuncturing
Terminator
UU(E)
Selector
UU(E)
Select Info Bytes
UU(E)
Re-order Bytes
Model InfoCreated by: Glen DyerCreated date: Sat Jun 10 16:43:33 2006Modified by: dye27622Modified date: Sat Jun 10 17:05:10 2006Model Version Number: 1.0
RS DecoderErr
Integer-OutputRS Decoder
Extract DataSymbols
Data
Extract Datafrom OFDM Symbol
16QAMDemodulator
Demodulate
z-478
Delay - Compensate for Viterbi Decoding
z-376
Delay
GeneralBlock
Deinterleaver
Deinterleave
Viterbi Decoder
Decoder inserts delay of 34
Unipolar toBipolar
Converter
Decoder expectsones and minus ones
Integer to BitConverter
Convert to Bits
Bit to IntegerConverter
Convert Bits toBytes
• This is the developed model for the 802.16 OFDM Receiver• The receiver implementation must invert the operations that are
defined for the transmitter, including the • Bit Scrambling• Concatenated Punctured Reed Soloman and Punctured
Convolutional Encoding• Bit Interleaving• Adaptive Modulation • OFDM Symbol Creation
• This is the developed model for the 802.16 OFDM Receiver• The receiver implementation must invert the operations that are
defined for the transmitter, including the • Bit Scrambling• Concatenated Punctured Reed Soloman and Punctured
Convolutional Encoding• Bit Interleaving• Adaptive Modulation • OFDM Symbol Creation
26
C-Band Modeling – Model Validation
802.16 OFDM 16-QAM Modulation Simulated BER Performance
• The developed simulation was exercised against AWGN and compared to published result for validation purposes
• This slide shows the raw (uncoded) BER performance of our simulation against theoretical results
• For contrast, and to get a sense of the achieved coding gain, the BER after the Viterbi and Reed Solomon decoding is also shown
• The developed simulation was exercised against AWGN and compared to published result for validation purposes
• This slide shows the raw (uncoded) BER performance of our simulation against theoretical results
• For contrast, and to get a sense of the achieved coding gain, the BER after the Viterbi and Reed Solomon decoding is also shown
27
C-Band Modeling – Results
• Finally, an approximation to the Ohio University suggested airport channel models was made, and 802.16 was evaluated against this model
• The channel model was for a large airport in the Non-LOS region
• The curves show expected performance for various maximum Doppler shifts, and represent 802.16 performance from a virtual standstill through expected velocities in the movement area
• Finally, an approximation to the Ohio University suggested airport channel models was made, and 802.16 was evaluated against this model
• The channel model was for a large airport in the Non-LOS region
• The curves show expected performance for various maximum Doppler shifts, and represent 802.16 performance from a virtual standstill through expected velocities in the movement area
28
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