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”.















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


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


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


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




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




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


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


Convert Bytes to Bits

Bit to IntegerConverter

Convert Bits to Bytes



• 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


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


Convert to Bits


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

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:...

Documents

band ag channel

band cost modeling process

modeling purposes

band system

khz channel

ht200 channel model

severe channel

terrestrial channel