Jens MittagDSN Research Group – Institute of Telematics – University of Karlsruhe
ns-3 and wifi -An overview of physical
layer models
Jens Mittag, Timo BingmannWorkshop on ns-3 – in conjunction with SIMUTools 2009
March 2nd, 2009
Decentralized Systems and Network Services Research Groupand Junior Research Group for Traffic TelematicsInstitute of Telematics – University of Karlsruhe
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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I. Background
» Characteristics of VANETs:– Very high mobility of network nodes
– Diverse environment
– urban scenarios
– rural scenarios
– highway scenarios
– Radio signal propagation conditions are
– changing rapidly over time
– different w.r.t. environmental effects
– Fully distributed communication system
» Our ns-2 / ns-3 experience– ns-2 PHY/MAC improvements, e.g. cumulative interference, capture capabilities
or Nakagami-m distribution (2006)
– Port of improvements to ns-3 finished – merge into main branch pending
» Research background: V ehicular A d-hoc NET works:– Protocol development, evaluation and optimization
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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I. Motivation
» How should we model the quality of the wireless communication channel?
» Based on which set of rules shouldwe decide whether a packet can besuccessfully decoded?
Radio PropagationModeling
Transceiver ReceptionModeling
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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II. Wifi Architecture of ns-3
MacHigh
Queue
DcaTxopDcfManager
StationManagerMacRxMiddle
MacLow
WifiPhy
WifiChannel
InterferenceHelper
ErrorRateModel
PropagationLossModel
MAC
PHY
WIRELESSCHANNEL
FOCUS OF THIS TALK
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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III. Radio Signal Propagation
» 3 different scales of signal strength variation
» PathLoss:– Friis
– Two-Ray Ground
– LogDistance
– ThreeLogDistance
» Shadowing:– LogNormal
Shadowing
» Fast fading:– Nakagami-m
– Rician Fading
– Rayleigh Fading
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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III. Radio Signal Propagation
» ns-3 calculates one signal strength for each packet
» Principle: chaining of several propagation loss models
» 3 different scales of signal strength variation
Friis Shadowing Nakagami-mTxPwr RxPwr
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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III. Radio Signal Propagation
» Issues with model usage
– Currently, (most) models are applied in a probabilistic way– no correlation for receivers in a close proximity
– no possible correlation of successive packet receptions
– No consideration of scenario semantics– e.g. no radio obstacles such as buildings, trucks, …
– No consideration of signal strength variations during packet reception– e.g. due to a time- and frequency-selective channel
Choosing the right model and parametrizationis a tough job and requires a thorough understanding
of the communication system and of influencingenvironmental effects!
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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IV. Transceiver Reception Modeling
» How to model the reception behavior of a transceiver?
– How to decide whether a packet can be successfully decoded?
» How are interfering packets and background noise modeled?– Additive White Gaussian Noise Channel model
1. Detection of the preamble2. 1st decision : could the header
be successfully decoded?3. 2nd decision : could the payload
be successfully decoded?
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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IV. Additive White Gaussian Noise Channel
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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IV. Additive White Gaussian Noise Channel
» Reception quality of packet
– Ratio of Signal Strength to Noise & Interference
SINR = Signal
Noise + Interference
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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V. Reception Criterion
» Bit-Error Rate based decision– For each packet segment with a constant SINR compute corresponding BER
– Mapping Φ: SINR → BER can be derived analytically or empirically for eachmodulation scheme (coded/uncoded)
– Combine the BERs into a Packet Error Rate (PER)
P = 1 – (1 – BER )err
ii
L i
byK
rishna Pillai(http://w
ww
.dsplog.com/)
Assumption: BitErrors are uniformlydistributed and independent!
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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V. Reception Criterion
» SINR based decision
– Determine the minimum experienced SINR level of a packet– Compare this SINR with a threshold
– Thresholds are measured experimentally using real hardware– e.g. 5dB for BPSK with Atheros chipsets
– e.g. 8dB for QPSK with Atheros chipsets
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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V. Reception Criterions
» Capture Effect
– So far, synchronization to a packet is only possible when receiver is in idle state, i.e., Phy is searching for a preamble
– Modern chipsets support a feature called „packet capturing“– even if receiver is already synchronized to a packet, it is able „switch“ over
to a new arriving packet
– SINR of new packet has to be sufficiently high → capture threshold
– Value for capture threshold is a trade-off– capture threshold too low → aggressive capture policy
– capture threshold too high → conservative capture policy
Jens Mittag, Timo BingmannDSN Research Group – Institute of Telematics – University of Karlsruhe
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VI. Conclusion
» We have different models to account for radio propagationcharacteristics
– Pathloss
– Shadowing
– Fast Fading
» We have different models to reflect transceiver technology– Additive White Gaussian Noise channel
– BER-based reception criterion
– SINR-based reception criterion
– Capture model
Again, choosing the right model and the rightparametrization is difficult. A wrong configurationof the wifi might lead to invalid protocol results!