a general model of wireless interference
DESCRIPTION
A General Model of Wireless Interference. L. Qiu, Y. Zhang, F. Wang, M. Han, R. Mahajan Mobicom 2007. A Model for ?. Misleading title Nothing new about wireless interference Indeed, a model for predicting the throughput/goodput of wireless networks Motivation - PowerPoint PPT PresentationTRANSCRIPT
A General Model of Wireless Interference
LL.. Qiu, Y Qiu, Y.. Zhang, F Zhang, F.. Wang, M. Wang, M. Han, RHan, R.. Mahajan Mahajan
Mobicom 2007Mobicom 2007
A Model for ?
• Misleading title– Nothing new about wireless interference
• Indeed, a model for predicting the throughput/goodput of wireless networks
• Motivation– Helpful in evaluating design/protocols (e.g.
channel assignment)– Direct measurements alone is insufficient
• Lacks predictive power and scalability
Problem Statement
• Given characteristics of– RSS between each pair
(RSSm,n)– Background noise (Bn)– Traffic demand between pairs
(dm,n)
• What is the pairwise throughput/goodput?
• My dumb solution– Calculate SINR at every node– Throughput = B*log2(1+SINR)
RSSm,n
Bn
• Problems– Non-constant RSS– Ignoring Underlying MAC
Contributions
• State of the Art: only handle restricted traffic – Only two senders or two flows – Only broadcast traffic– Only saturated demands
• Contributions– Interference among an arbitrary number of senders – Both broadcast and unicast traffic– Both saturated and unsaturated demand
Reality
Dumb solution
This paperState of the Art
Limit of analytical methods
Its limit
Overview of the Model
• How it works– Measure pairwise RSS via broadcast probes
• One node broadcast at a time, others measure RSS O(n) probes
– Saturated broadcast sender/receiver models• Markov-chain model
– Extend to unsaturated broadcast– Extend to saturated/unsaturated unicast
given network
RF profile measurement
traffic demand
sender model
receiver model
throughput
goodput
pairwise RSS
Broadcast Sender: Overview• Estimate how much a sender can send
– MAC: 802.11 DCF
• Markov chain (simplification #1)– State i: a set of active nodes Si
– Stationary probabilities: i (fraction of time that the system is in state i)– Throughput of node m: tm = ∑i|mSi i
00 01
1011
0…0
0…1
0..10
1…1
.
...
Broadcast Sender: Overview Broadcast Sender: Overview (Cont.)(Cont.)
• State transition probability– Staying idle: P00(n|Si)– Idle to active: P01(n|Si)– Active to idle: P10(n|Si)– Staying active: P11(n|Si)– Assume node independence (simplification #2)
• Compute stationary probabilities i by solving LP– Highly efficient for sparse M
Broadcast Sender: Transition ProbabilitiesBroadcast Sender: Transition Probabilities
)|(1)|(
)()|(
)|(1)|(P
demands saturatedunder 1)( where
)()()(
1)S|C(m
0]counter &clear is medium |data has Pr[m
clear] is medium|0Pr[counter
clear] is Pr[medium
data] has m & 0counter &clear is mediumPr[
)|(
1010
10
0100
_____________________i
01
ii
sloti
ii
i
SmPSmP
mT
TSmP
SmPSm
mQ
mQmOHmCW
SmP
2/minCW
tT slotDIFS /
Under the assumption that both transmissionand idle times are exponential (simplification #4)
Simplification #3
Broadcast Sender: Clear Broadcast Sender: Clear ProbabilityProbability
•
• How to estimate Im|Si?
– Im|Si=Wm+Bm+∑sSi\{m} Rsm
– Assume each term is lognormal variable (simplification #5)
– Approximate the sum using a lognormal variable by matching mean and variance
]Pr[)|( | mSmi iISmC
Broadcast Sender: Broadcast Sender: Handle Similar Packet SizesHandle Similar Packet Sizes
• Synchronization occurs when packet sizes used by different nodes are similar– When several nearby nodes transmit together, they will end
transmission together– Independence assumption fails
• Handle synchronization– Construct synchronization graph Gsyn
• Two nodes are connected iff C(m|{n}) 0.1 and C(n|{m}) 0.1– Find all synchronization groups
• Each connected component in Gsyn is a synchronization group (simplification #6)
– If m and n in the same synchronization group• mSj and n Sj’ M(i,j) = 0• P10(mn|Si) = Tslot/T(m) instead of (Tslot/T(m))|G|
Broadcast Sender: Broadcast Sender: Handle Unsaturated DemandsHandle Unsaturated Demands
• Estimate Q(m): probability m has data to send when its backoff counter is 0 and channel is clear at m
• Under saturated demands, Q(m) = 1 • Under unsaturated demands, compute Q(m) iteratively to
ensure that demands are not exceeded
Initialize Q(m) = 1
Solve the Markov chain
Update Q(m)
Brief overview of other parts
• Broadcast Receiver– Estimate packet loss rate
• Extending to unicast: challenges– Binary backoff
• Sending rate depends on loss rates
– DATA losses due to collisions with ACKs• Model ACK sending rate, which in turn depends on
DATA sending rate and loss rates
– Traffic demand• Account for retransmissions
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
Simulation Evaluation: Simulation Evaluation: Saturated BroadcastSaturated Broadcast
10 saturated broadcast
Accurate for 10 saturated broadcast
(a) throughput (b) goodput
0
0.1
0.2
0.3
0.4
0.5
0.6
0 500 1000 1500 2000 2500G
oodp
ut
Sender-Receiver Pair ID
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
Ours (RMSE=0.0460)Actual
Testbed EvaluationTestbed EvaluationUW traces: 2 senders, 30 mW, broadcast, saturated
(a) throughput (b) goodput
More accurate than UW-model for 2-sender
Summary
• A model for predicting the throughput of wireless networks
• Validated by simulation and testbed evaluation
Reality
Dumb solution
This paperState of the Art
Limit of analytical methods
Its limit
Less simplifications
RTS/CTS, different MAC
Traffic modelling, human behavior