DISCERN: Cooperative Whitespace Scanning in Practical Environments

Tarun Bansal, Bo Chen and Prasun SinhaOhio State Univeristy

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Challenge : Limited Capacity due to Growing Demand

Devices Proliferation

VideoUploads

Mobile Data Traffic

Streaming VideoIncreasing Wireless

Demand

20X - 40XOVER THE NEXTFIVE YEARS

50 BILLIONCONNECTED DEVICES

BY 202035X

2009 LEVELSBY 2014

24 HOURSUPLOADED EVERY60 SECONDS

Slide courtesy of “White Space Networking: The Road Ahead” by Ranveer Chandra, Microsoft Research

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White Space Channels• Discrepancy in channel usage

– Unlicensed (ISM) bands are congested – Licensed bands are free most of the time

• What if unused channels are used for data transmission?

Taken from “How much white-space capacity is there?” IEEE DySPAN, 2010

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Opportunistic Usage• Unlicensed users must avoid interference to licensed user (or

primary user, PU)

• Scan frequently to detect arrival of primary user• Scanning takes time and results in throughput loss

• Scanning must be reliable• Use Cooperation

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Problem Statement

• Multiple SUs available to scan multiple channelsDevelop a solution that computes scanning

assignment SS = (ni, cj): ni scans channel cjSubject to– Strict budget constraints in terms of time allocated for

scanning: |S| < ρ– Take into account practical considerations

Practical Considerations

• Presence of obstacles• Multiple PUs per channel– Must select SUs such that all PUs are covered– Can aggregate readings of only those SUs that are in the range

of same PU6

PU1 PU2

n1

n2

n3

n4

n6

n5

SBS

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Which user should scan

• Budget constraint: SBS has to select 3 SUs• Optimal solution:

– Must cover both PUs and take into account presence of obstacle– Use n1 and n2 to scan PU1 and n3 to scan PU2

– Optimal Solution: n1, n2, n3

PU1PU2

n1

n2n3

n4

n6

n5

SBS

Do existing solutions work?

• Three existing solutions– Maximize coverage (Geographical Select)– SUs with high RSSI of the PU signal (Min et al.)– SUs with minimum correlation among themselves

(Cacciapuoti et al.)

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Existing Solutions: Maximize coverage

Selected SUs: n1, n3, n6Does not cover PU1 with high accuracy

PU1

PU2

n1

n2

n3

n4

n6

n5

SBS

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Existing Solutions: SUs with high RSSI of the PU signal

Selected SUs: n3, n4, n5Does not cover PU1

PU1

PU2

n1

n2

n3

n4

n6

n5

SBS

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Existing Solutions: SUs with minimum correlation among themselves

Selected SUs: n1, n3, n6Does not cover PU1 with high accuracy

Existing solutions are incapable of accounting for practical considerations.

PU1

PU2

n1

n2

n3

n4

n6

n5

SBS

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DISCERN Overview

• Step 1: Differentiate SUs that are in the range of same PU– Handles presence of multiple PUs

• Step 2: Define a metric that quantifies the scanning accuracy of an assignment

• Step 3: Greedy algorithm to compute the scanning assignment

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DISCERN Step 1• Differentiate SUs that are in the range of same PU

– Given two SUs, are they in the range of same PU?

– Difficult since SUs in the range of same PU may have low correlation• Say n5 reports: 1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1• n6 reports: 1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0• Correlation = 0.169 (Not high enough)

– Need a new metric to determine if two SUs are in the range of same PU• Between 0 and 1: 0 when two SUs are definitely in range of different PU, 1 when two SUs

are definitely in the range of same PU

n6

n5

SBS

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Knowledge Factor

– Knowledge Factor: Knowledge added by ni to nj about the state of the PU

– Assume ni and nj are in the range of same PU with • If xj = 0, then P(xi =0 ) is high

– would be low

• Kij would be low

– If ni and nj are in the range of same PU, then at least one of Kij or Kji would be low

( 1| 0)

min 1,( 1)ij

i j

i

P x xP

Kx

d di jP P

( 1| 0)i jP x x

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Knowledge Factor

• When ni and nj are in the range of different PU

• Kij ≈ Kji ≈ 1

• Value of knowledge factor allows DISCERN to differentiate the relationship between the two SUs

( 1| 0) ( 1)

min 1, min 1, 1( 1) ( 1)

i j iij

i i

P x x P xK

P x P x

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DISCERN overview

• Step 1: Differentiate SUs that are in the range of same PU

• Step 2: Define metric that quantifies the scanning accuracy of an assignment– Handles differences in the accuracy of different SUs

• Step 3: Greedy algorithm to compute the scanning assignment

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Ω (S) -metric

• Metric that computes the effectiveness of a scanning assignment– Denoted by Ω(S)– Higher Ω(S) implies that channel state estimation based on

S is correct• Challenge– SUs in S have different accuracies (Pi

d and Pi

f)– SUs in S may cooperate– Do not know how many PUs are there

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Using cooperation• Probability that ni can predict the state of the PU in the range of nj (∆j)

– Depends upon• Probability that ni and nj are in the range of same PU

– Given by Pij (Probability that ni and nj are in the range of same PU)

• How accurate is ni itself– Given by Pi

d −Pi

f

– Accuracy of ni in predicting the state of ∆j is given by: Pij (Pid

−Pif )

∆ jni

nj

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Accuracy of predicting the state of a PU

• But nj can take help from all other SUs as well

• Ω(S,k,j) = Probability that SUs in S can cooperatively predict the state of the PU in the range of nj

Primary User

nj

n1

n2

n3

Accuracy of predicting the state of a PU

• Ω(S,k,j) should be between 0 and 1• Ω(S,k,j) should be 1 if accuracy of any SU in S is 1• With increase in the cardinality of S, Ω(S,k,j) should increase

since more observations about the state of ∆j are available.

i

in

(S,k,j) 1 - (1 - (Accuracy of n ))kS

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Accuracy of predicting the state of all PUs over a single channel

• Ω (S,k) = Probability of correctly estimating the state of the channel ck after aggregating readings from SUs in S

jn

1 (S,k) (S,k,j)N

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Computing the metric over all channels

• Ω (S) = Average over all channels

k M

1 (S) (S,k)M

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DISCERN overview

• Step 1: Differentiate SUs that are in the range of same PU

• Step 2: Define metric that quantifies the scanning accuracy of an assignment

• Step 3: Greedy algorithm to compute the scanning assignment

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Greedy Algorithm to compute S

• Add pairs of (ni, ck) to S– At every step, add (ni, ck) that maximizes the value of Ω(S)– Using submodular optimization technique, we bound the

approximation ratio by 0.63

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Experiments: Setup

• To show correctness of knowledge factor• Two USRP nodes placed at different locations• Collect data over multiple channels• Four different scenarios that capture different

relationship of the two nodes

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Setup and Results• Scenario 1: Both SUs adjacent to each other on the roof of a 8-floor building

• Scenario 2: One SU is in the basement while the other is on the roof

• Scenario 3: Both SUs are in the basement of the 8-floor building

• Scenario 4: One SU is on the roof of the building , other is in an open parking lot at a distance of 80 miles.

• We observed that correlation with optimal threshold correctly classified the SUs in 69% cases while knowledge factor in 95% cases.

• Knowledge factor improves the accuracy by over 25%.

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Simulations setup• Trace-driven simulations

• SBS located at the center and varying number SUs were randomly deployed around it in a circular field of 20 miles.

• Channel Model: 10 channels

• PU Model: 40 PUs

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Other Algorithms• Geographical Select: Algorithm selects that SU for scanning which has the

maximum distance from the already selected nodes

• Min et al.: Selects nodes with the highest received signal strength (RSS) of the PU signal

• Cacciapuoti et al.: Selects nodes that have minimum correlation with each other

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Simulation Results• Variation with SU density

On average, DISCERN improves the accuracy by at least 30% (Geographical Select), 130% (Min et al.) and 40% (Cacciapuoti et al.).

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Conclusion• Novel knowledge based mechanism

• Using this knowledge based method, defined a metric (Ω) that captures the accuracy of a given scanning assignment

• Experiments show that Discern improves the accuracy of determining if two SUs are in the range of the same PU by over 25%

• Simulations show that Discern improves the accuracy of channel state estimation by at least 30% when compared to other algorithms.

Questions

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Simulations setup• Trace-driven simulations

• SBS located at the center and 300 SUs were randomly deployed around it in a circular field of 20 miles.

• Channel Model– 10 channels– Slow fading and fast fading

• PU Model– 40 PUs on these 10 channels within a radial distance of 20 miles from the

center– PU location and their power level established using FCC database– PU on/off state using traces collected using USRP radio

|S| < ρ