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

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 = , σ τ 0 = 0.1 μs and = 6 dB Tap #Delay (µs)Power (lin)Power (dB) Fading Process Doppler Category 1010RiceanJakes RayleighJakes RayleighJakes RayleighJakes RayleighJakes RayleighJakes RayleighJakes

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

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

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

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 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 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 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 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 = [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 = [Proakis] –Model uses the same traceback length (n = 5) and modulation index (h = 0.715) Using a modulation of minimizes probability of error for binary CPFSK [Schonhoff 1976]

17 LDL Modeling – Investigation of Coding Gain A modulation index of was required to validate the model with published results, but LDL calls for a modulation index of 0.6 –Changing the modulation index from 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 was required to validate the model with published results, but LDL calls for a modulation index of 0.6 –Changing the modulation index from 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 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

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

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

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

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

24 C-Band Modeling – e Transmitter Model

25 C-Band Modeling – e Receiver Model

26 C-Band Modeling – Model Validation

27 C-Band Modeling – Results

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