LIASIT.LU

EMPS/ASMS 2004 September 04

Packet Coded Mobile Satellite Broadcast Systems: Error Rate Computations and

Quality of Service Based DesignCristoff Martin*, LIASIT, Luxembourg, cristoff@s3.kth.se

Alexander Geurtz, SES GLOBAL S.A., LuxembourgBjörn Ottersten, KTH, Royal Institute of Technology, Sweden

* The support of SES GLOBAL S.A. and KTH to this work is gratefully acknowledged

Cristoff Martin 2

Presentation Outline

• Background and Goals.– Mobile satellite broadcast to moving vehicles– Estimation, optimization of resource requirements

• System Model:– Channel characterized by long term shadowing and blockages.– Packet coded system.

• Estimating receive probability• Applications

– Optimal packet size?– Minimum redundancy to broadcast a file within a delay?– Minimum delay for a streaming service at a desired data rate?– How much bandwidth/delay can be saved if transmit diversity

(satellite or terrestrial repeaters) is employed?• Concluding remarks

Cristoff Martin 3

Background and Goals

Background:• Mobile satellite broadcast to vehicular receivers• Channel characterized by long term blockages and

shadows from obstacles in terminal environment.• Broadcast less delay sensitive – Long delay and coding

option to overcome channel impairmentsGoals of this work:• Analysis: Estimate error rate performance in measured

environments with reduced need of simulations• Design: Minimize resource requirements while satisfying

QoS constraints• Architecture: Relaxation on resource requirements using

transmit diversity

Cristoff Martin 4

System Model – Mobile Satellite Channel

• Two-state Markov model characterized by the transition probabilities between a “blocked” and a “non-blocked” state:

• Blocked state: Channel coding insufficient

• Non-Blocked state: Channel coding sufficient

• Example: Ku-Band measurements by DLR, average time per state:

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System Model – Transmission Scheme

• Encode the smallest useful data unit, i.e. a file in a file delivery system or a frame in a streaming system

• Many small packets: Correlation between packets• Few large packets: Partially received packets

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Estimating the Error Rate I

• Exact error computation requires time consuming simulations, instead simplifying assumptions:1. Any symbol received in B state ⇒ Packet lost2. All symbols received in G state ⇒ Packet received3. κ = κ(k,l) packets received ⇒ File recovered

• Convert channel model to packet Markov model

• G’: last packet received, B’: last packet lost• g’ and b’ computed analytically• Receive probability ⇔ being in state G’ at least κ times

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Estimating the Error Rate II

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Applications – System Setup

• System setup to illustrate examples:– 2 Megabits per second data rate in “non-blocked” state.– Temporal behavior (DLR Ku-band measurements):

– Population of equal size in all environments• Example data units:

– 1 Megabyte file ⇒ File delivery service– 2 Kilobyte frame ⇒ Streaming type of service

• Optimal erasure codes assumed, i.e. κ = k

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Application – Optimal Packet Length

• “Streaming”• 2 kbyte frame• 30 second

delay• nt = 3nu

• Optimal packet size!• Average / maximum error probability cost function.

Retransmission

Symbol interleaving

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QoS Based Design – Minimize Bandwidth Requirements

• “File Delivery”• 1 Megabyte

file• 3 Minute delay• Average error

design criterion

• Minimal overhead to satisfy error rate constraint!

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QoS Based Design – Minimizing the Delay

• “Streaming”• 2 kbyte frame• nt = 3nu

• Maximum error design criterion

• Minimal delay to satisfy error rate constraint!

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Gains from Satellite / Terrestrial Transmit Diversity

• Satellite Diversity:– Transmit the jointly coded packets over the different satellites:

– (Optimistic) Assumption: Number of received packets per satellite independent. Example, 2 satellites, rural, k + l = 24, 30 seconds:

• Terrestrial Repeaters– All, or parts, of some environments have perfect terrestrial coverage

* =

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Gains from Satellite / Terrestrial Transmit Diversity

• File Delivery: nu = 1 Mbyte, 3 minute delay, optimized nt(Average error):

• Streaming: nu = 2 kbytes, 98% received frames, optimized delay (Max error):

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Conclusions

• Simple channel model and assumptions allows for efficient system error computations.

• Based on channel measurements mobile satellite broadcast systems can be designed and analysed.– Predict system error rate performance.– Minimize use of system resources while satisfying QoS constraints– Upper bound on gain from satellite diversity– Resource relaxation when terrestrial repeaters are available

• Missing pieces / Future work:– Channel coding to overcome shadowing and blockages?– Synchronization?– Correlation between channels when satellite diversity is employed?– Stationary / slowly moving receivers?– Scheduling?