A General Model of Wireless Interference

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A General Model of A General Model of Wireless InterferenceWireless Interference

Lili QiuLili Qiu, Yin Zhang, Feng Wang, Mi Kyung Han , Yin Zhang, Feng Wang, Mi Kyung Han

University of Texas at AustinUniversity of Texas at Austin

Ratul MahajanRatul Mahajan

Microsoft ResearchMicrosoft Research

ACM MOBICOM 2007ACM MOBICOM 2007

September 12, 2007September 12, 2007

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MotivationMotivation• Interference is critical to wireless

network performance

• Understanding the impact of wireless interference directly benefits many network operations– Routing– Channel assignment– Transmission power control– Transport protocol optimization– Network diagnosis

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State of the ArtState of the Art• Non-measurement based interference models

– Inaccurate for real networks [Kotz03,Padhye05]• Interference range is twice the communication range

– Restricted scenarios• Single-hop networks (e.g., Bianchi’s model)• Multihop networks with 2-flows (e.g., Garetto et al.)

• Direct interference measurement– Lack scalability and predictive power

• Measurement-based interference model [Reis06] – Promising to achieve both accuracy and scalability– Restricted scenario

• Only two saturated broadcast sendersNeed a general measurement-based model!

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Our ContributionsOur Contributions• A general interference model for IEEE 802.11

multihop wireless networks– Models non-binary interference among an arbitra

ry number of senders – Models both broadcast and unicast traffic– Models both saturated and unsaturated demands

• Easy to seed– Requires only O(N) broadcast measurements

• Highly accurate– Validated through experiments and simulations

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Background on IEEE 802.11 DCFBackground on IEEE 802.11 DCF• Broadcast

– If medium is idle for DIFS, transmit immediately– Otherwise, wait for DIFS and a random backoff between [0,

CWmin]

• Unicast– Use ACKs and retransmissions for reliability– Binary backoff

• CW doubles after each failed transmission until CWmax• Restore CW to CWmin after a successful transmission

DIFS Data TransmissionRandomBackoff

DIFS Data TransmissionRandomBackoff

ACKTransmission

SIFS

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Model OverviewModel Overview

given network traffic

demand

sender model

receiver model

throughput

goodput

pairwise RSS

• How it works– Measure pairwise RSS via broadcast probes

• One node broadcast at a time, other nodes measure RSS only requiring O(n) probes

– Use sender/receiver models to get throughput/goodput• Basic model: broadcast traffic• Extension to unicast traffic

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Assumptions Assumptions • A sender can transmit if

– The total energy it received CCA threshold• A receiver can correctly receive a

transmission if its– RSS radio sensitivity– SINR SINR threshold

• Can easily extend to BER-based model

• Assume 1-hop traffic demands– Traffic is only sent over 1 hop and not

routed further– Multi-hop demands need to be first mapped

to 1-hop demands based on routing

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Broadcast Sender: A Markov Broadcast Sender: A Markov ModelModel

• Challenge– Sender behavior depends on the set

of nodes currently transmitting• Solution

– Associate a state i with every possible set of transmitting nodes Si

– Enhance scalability by pruning low-probability states and transitions (see paper)

• Algorithm1. Compute individual node’s mode transitions in each st

ate2. Compute state transition probabilities M(i,j)3. Compute stationary probabilities i by solving LP4. Compute throughput of node m: tm = ∑i: mSi i

{} {1}

{1,2}{2}

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Individual Node Mode TransitionsIndividual Node Mode Transitions

• – Im|Si=Wm+Bm+∑sSi\{m} Rsm

– Each term is modeled as a lognormal r.v. (validated experimentally)– Approximate the sum using a single moment-matching lognormal r.v.

•

• – Q(m) = 1 for saturated demands– Q(m) is estimated iteratively for unsaturated demands

]Pr[)|(]S|clear is mediumPr[ |i mSmi iISmC

DIFSCW

2/

1]clear is medium |0counterPr[

0]counter &clear is medium|data has Pr[m Q(m)

0 1

P00=1-P01 P11=1-P10

P01

P100: idle 1: transmitting

Q(m)DIFSCW/

)C(m|S

SmP

i

i

2

1

]S | data has m & 0counter

&clear is mediumPr[

)|(

i

01

per tx slots#

1)|(10 iSnP

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State Transition ProbabilitiesState Transition Probabilities• Case 1: packet sizes are exponentially distributed

– Different nodes’ mode transitions are independent

– M(i, j) = n P{n’s mode in state i n’s mode in state j}

• Case 2: packet sizes are similar– Mode transitions are dependent due to synchronization

• Two nodes are in sync iff C(m|{n}) 0.1 and C(n|{m}) 0.1• A and B are in sync and have overlapping transmissions

their transmissions start and end at the same time

– Solution: fate sharing• m and n are in sync and both active in state i

they have the same mode in next state • Probability for a group of k nodes in sync to all transition from 1

to 0 is instead of

per tx slots#

1 k)per tx slots#

1(

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Broadcast ReceiverBroadcast Receiver• Estimate slot-level loss rate

– Loss due to low RSS• Based on pairwise RSS measurement

– Loss due to low SINR• Our measurements show that RSS follows log-normal

distribution• Approximate with a single moment-matching lognormal r.v.

• How to get packet-level loss rate?– Pkt loss rate slot-level loss rate under partial collisions

(common in hidden terminal)

– Differentiate losses due to synchronized collisions and asynchronized collisions

• Synchronized collisions when a node is synchronized with at least one other node in the state

• Otherwise, asynchronized collisions

]Pr[| nmnrssSimn Rl

}Pr{|

| nSmn

mnSmn

i

i I

Rl

iSmn

mn

I

R

|

Slot loss=10%Pkt loss = 100%

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Extensions to Unicast DemandsExtensions to Unicast Demands• Sender side extensions

– Compute average CW based on binary backoff– Incorporate ACK/SIFS overhead (in addition to DIFS)– Derive Q(m) to ensure demands (w/ retx) not exceeded

• Receiver side extensions– Include low RSS induced losses for both data and ACK– Include low SNR induced loss due to collisions between

data/ACK, ACK/data, ACK/ACK (in addition to data/data)

• Challenge– Inter-dependency between sending rate and loss rates

• Sending rates depend on loss rates due to binary backoff• Loss rates depend on sending rate due to interference

• Solution: use an iterative framework

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Unicast Model: Iterative FrameworkUnicast Model: Iterative Framework

)()1()()(

)1(

mQmQmQ

LLLnew

mnnewmnmn

Compute CW and OH using Lmn

Compute i using M

Update and Qnew(m)

Initialize Lmn = 0, Q(m) = 1

Derive state transition matrix M

newmnL

if (!converged)

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Evaluation MethodologyEvaluation Methodology• Qualnet simulation

– Controlled environment and direct assessment of individual components in our model

– Vary topologies, # senders, demand types, freq. band

• Testbed experiments– More realistic scenarios

• RF fluctuation, measurement errors, and variation across hardware

– UT traces• 22-node, 802.11 a/b/g NetGear WAG511, Madwifi, click• Vary # senders, demand types

– UW traces (Reis et al.)• 14-node testbed inside an office building• 2 saturated broadcast sender traces

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Evaluation Methodology (Cont.)Evaluation Methodology (Cont.)• Compare with actual values and UW model

– Scatter plot– Root mean square error (RMSE):

• Overview of UW model – Applies only to 2 saturated broadcast senders– Uses O(N) probes to measure pairwise RSS– Sender model

• Estimate the deferral probability based on RSS from the other sender

– Receiver model• Estimate loss rate based on SNR by treating RSS from

the other sender as part of interference

P

actualesti

ii 2)(

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Simulation Evaluation: Simulation Evaluation: Saturated BroadcastSaturated Broadcast

2 saturated broadcast

More accurate than UW 2-node model

(a) throughput (b) goodput

0

0.2

0.4

0.6

0.8

1

1.2

0 100 200 300 400 500G

oodp

ut

Sender-Receiver Pair ID

Ours (RMSE=0.0050)UW (RMSE=0.1664)Actual

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0 2 4 6 8 10 12 14 16 18 20

Thro

ughp

ut

Sender ID

Ours (RMSE=0.0028)UW (RMSE=0.1450)Actual

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Simulation Evaluation: Simulation Evaluation: Saturated BroadcastSaturated Broadcast

10 saturated broadcast

Accurate for 10 saturated broadcast


0

0.1

0.2

0.3

0.4

0.5

0.6

0 500 1000 1500 2000 2500G

oodp

ut


Ours (RMSE=0.0189)Actual

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50 60 70 80 90 100

Thro

ughp

ut

Sender ID


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Simulation Evaluation:Simulation Evaluation:Unsaturated UnicastUnsaturated Unicast

10 unsaturated unicast

Accurate for unsaturated unicast


0

0.1

0.2

0.3

0.4

0.5

0.6

0 10 20 30 40 50 60 70 80 90 100G

oodp

ut



0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50 60 70 80 90 100

Thro

ughp

ut

Sender ID


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Testbed EvaluationTestbed EvaluationUW traces: 2 saturated broadcast senders (30 mW)


More accurate than UW-model for 2-sender

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Testbed Evaluation (Cont.)Testbed Evaluation (Cont.)UT traces: 5 saturated broadcast senders (30 mW)


Accurate for saturated broadcast

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Testbed Evaluation (Cont.)Testbed Evaluation (Cont.)UT traces: 3 unsaturated broadcast senders (1mW)


Accurate for unsaturated broadcast

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Summary of Evaluation ResultsSummary of Evaluation Results• Achieve high accuracy under all types of

traffic demands– Unicast & broadcast; saturated &

unsaturated

• More accurate than state-of-art model for 2 saturated broadcast senders

• Higher errors in testbed than in simulations due to– RF fluctuation– Errors in estimating actual RSS especially

under high loss rates

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ConclusionsConclusions• Main contributions

– A general interference model that handles • An arbitrary number of senders• Broadcast + unicast traffic• Saturated + unsaturated demands

– Validated by simulation and testbed evaluation

• Future work– Improve the accuracy of RSS estimation– Model-driven wireless network optimization

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Thank you!Thank [email protected]@cs.utexas.edu

Documents

A General Model of Wireless Interference