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DISCERN: Cooperative Whitespace Scanning in Practical Environments
Tarun Bansal, Bo Chen and Prasun SinhaOhio State Univeristy
2
Challenge : Limited Capacity due to Growing Demand
Devices Proliferation
VideoUploads
Mobile Data Traffic
Streaming VideoIncreasing Wireless
Demand
20X - 40XOVER THE NEXT
FIVE YEARS
50 BILLIONCONNECTED DEVICES
BY 2020
35X2009 LEVELS
BY 2014
24 HOURSUPLOADED EVERY
60 SECONDS
Slide courtesy of “White Space Networking: The Road Ahead” by Ranveer Chandra, Microsoft Research
3
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
4
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
5
Problem Statement
• Multiple SUs available to scan multiple channelsDevelop a solution that computes scanning
assignment SS = (ni, cj): ni scans channel cj
Subject 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
7
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
n2
n3
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, n6
Does not cover PU1 with high accuracy
PU1
PU2
n1
n2
n3
n4
n6
n5
SBS
10
Existing Solutions: SUs with high RSSI of the PU signal
Selected SUs: n3, n4, n5
Does not cover PU1
PU1
PU2
n1
n2
n3
n4
n6
n5
SBS
11
Existing Solutions: SUs with minimum correlation among themselves
Selected SUs: n1, n3, n6
Does not cover PU1 with high accuracy
Existing solutions are incapable of accounting for practical considerations.
PU1
PU2
n1
n2
n3
n4
n6
n5
SBS
12
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
13
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 x
PK
x
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
16
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
17
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 Pid
−Pif
– Accuracy of ni in predicting the state of ∆j is given by: Pij (Pid −Pi
f )
∆ 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
22
Computing the metric over all channels
• Ω (S) = Average over all channels
k M
1 (S) (S,k)
M
23
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
24
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
25
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
26
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
28
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
29
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.).
30
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
33
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