using multiple channels and spatial backoff to improve wireless network performance
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Using Multiple Channels and Spatial Backoff to Improve Wireless Network Performance. Nitin Vaidya University of Illinois at Urbana-Champaign www.crhc.uiuc.edu/wireless. MURI Review Meeting, September 12, 2006. Sharing the Spectrum. Classification of approaches - PowerPoint PPT PresentationTRANSCRIPT
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Using Multiple Channels and Spatial Backoff to Improve Wireless Network Performance
Nitin VaidyaUniversity of Illinois at Urbana-Champaign
www.crhc.uiuc.edu/wireless
MURI Review Meeting, September 12, 2006
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Sharing the Spectrum
Classification of approaches
Temporal : Traditional contention resolution
Spatial : Spatial backoff
Spectral : Multi-channel systems
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Multi-Channel Wireless Networks:
Capacity withConstrained Channel Assignment
Joint work withVartika Bhandari
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Channel Model
c channels available
Bandwidth per channel W
Channel 1
Channel 2
Channel c
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Channel-Interface Scenarios
Common scenarios today
11
11
c c
Single interface Multiple interfaces
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Fewer Interfaces than Channels
An interface can only use one channel at a time
Channel 1
Channel c
Single interface, multiple channels
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Interface Constraint
Throughput is limited by total number of interfaces in a neighborhood
Interfaces, a limited resource
k nodes in the “neighborhood”
throughput ≤ k * W
(for single interface per node)
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Capacity with Multiple Channels
How does capacity scale when number of channels c is increased?
Depends on constraints on channel assignmentto the interfaces
Capacity as defined by [Gupta & Kumar]
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Unconstrained Channel AssignmentPre-MURI work [Kyasanur05MobiCom]
Channels
Netw
ork
Cap
aci
ty
Single interfacenodes can utilizemultiple channelseffectively
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Constrained Channel Assignment
Hardware limitations Low cost, low power transceivers Limited tunability of oscillator
Policy issues Dynamic spectrum access via cognitive radio:
secondary users in a band only when primary inactive
1 2 3 4 5 6
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Network Model
c
ch
an
nels
W b
an
dw
ith
per
chan
nel
n nodes randomly deployed over a unit area torus
Interface can switch between f channels: 2 ≤ f ≤ c
c = O(log n)
1
2
3
4
5
6
…
…
…
c
Each node has one interface
s(1)
s(2)
s(f)
……
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Network Model
Motivated by [Gupta & Kumar]
Each node is source of exactly one flow
Chooses its destination as node nearest to a randomly chosen point
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Impact of Switching Constraints
Connectivity: A device can communicate directly with only a subset of the nodes within range
Bottleneck formation: Some channels may be scarce in certain regions, causing overload on some channels/nodes
(1, 2)
(2, 3)
(1, 3)
(2, 5)(7, 8)
(6, 7)(3, 6)
(5, 6)
(4, 5)
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Proposed Models
Adjacent (c, f) assignment
– A node can use adjacent f channels– Model encompasses untuned radio model
Random (c, f) assignment
– A node can use randomly chosen f channels
Spatially correlated assignment
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Adjacent (c, f) Assignment
f=2 c=8
Each node assigned a block of adjacent f channels c – f + 1 possibilities
A node can use channel i with probability= minimum {i, c-i+1, f, c-f+1} /c
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Random (c, f) Assignment
Each node uses a random f-subset of channels
A node can use channel i with probability f/c
f=2 c=8
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Spatially Correlated Assignment
N randomly located pseudo-nodes, each assigned a channel
Nodes close to a pseudo-node blocked from using thepseudo-node’s channel
Captures primary-secondary users
1
2
R
R
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Results at a Glance
Unconstrainedassignment Adjacent (c,f) Random (c,f)
Use c channels
Use f common channels
f
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Adjacent (c, f) Assignment
Necessary condition on range r(n)
Capacity upper bound
=c
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Lower Bound Construction
Divide torus into square cells of area a(n)
Cell structure based on [El Gamal]
r(n)
Transmission range r(n)
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Lower Bound Construction
Notion of preferred channels:
Probability that a node has that channel is at least f/2c
Includes most channels (except the fringe)
Each node has at least f/2 preferred channels
By choice of a(n):Every cell has Θ(log(n)) nodes capable ofswitching on each preferred channel
w.h.p.
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Routing of Flows
Straight-line routes forlong flows.
Detour routing for shortFlows
Ensure (c/f) hops S
D
P
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Channel Transition Strategy
(1, 2, 3)
(4, 5, 6)
Adjacent (6, 3) assignmentPreferred channels : 2,3,4,5
(3, 4, 5)
(4, 5, 6)
(2, 3, 4)(2, 3, 4)
(1, 2, 3)
2
23
4
5
5(4, 5, 6)
(2, 3, 4)
( 3, 4, 5)
Use randomlychosen preferred
channel available atsource (channel 2)
Start transitions toget to a preferred channelat destination (channel 5)
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Random (c,f) Channel Assignment
Required range for connectivity smaller than adjacent (c,f)
However, at minimum range, all channels not sufficiently represented in each cell
Our lower bound construction is not tight:Uses larger range than minimum for connectivity
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Conclusion: Multi-Channel Networks
Unconstrainedassignment Adjacent (c,f) Random (c,f)
Use c channels
Use f common channels
f
Even when f=2, get capacity benefit of √c
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Conclusion: Multi-Channel Networks
Unconstrainedassignment Adjacent (c,f) Random (c,f)
Use c channels
Use f common channels
f
Even when f=2, get capacity benefit of √c
cf
cf
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Conclusion: Multi-Channel Networks
Constrained channel assignment may be mandated by cost/complexity/policy constraints
Possible to get significant benefits with little flexibility in channel switching
Open issues
Closing the gap for random assignment
Spatially correlated assignment
Protocol design
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Sharing the Spectrum
Classification of approaches
Temporal : Traditional contention resolution
Spatial : Spatial backoff
Spectral : Multi-channel systems
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Spatial Contention Resolution
with
Carrier Sense Protocols
Joint work withXue Yang
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Contention Resolution
Temporal Approach:
Adapt channel access probability number of transmissions in a contention region = 1
Spatial Approach:
Adapt contention region number of transmissions in a contention region = 1
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Contention Resolution
Temporal Approach:
Adapt access probability
Number of transmissions in a
contention region = 1
Spatial Approach:Adapt contention
region
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Larger Occupied Space
Fewer concurrent transmissions at higher rate
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Smaller Occupied Space
More concurrent transmissionsat lower rate
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D perceives idle channel although A is transmitting
AB C
D
distance
Sig
na
l Str
eng
th
CS Threshold
Carrier Sense Multiple Access (CSMA)
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AB
CD
distance
Sig
nal
Str
eng
th
CS Threshold
How Carrier-Sensing Controls Occupied Space
EF
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Larger CS threshold by other stations leads to smaller occupied space by station A’s transmissions
AB
CD
distance
Sig
nal
Str
eng
th
CS Threshold
How Carrier-Sensing Controls Occupied Space
EF
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AB
CD
distance
Sig
nal
Str
eng
th
CS Threshold
Transmission Rate Needs to Be Adjusted Suitably
EF
Larger CS threshold leads to higher interference
Transmission rate depends on Signal-to-Interference-Noise Ratio
Lower rate
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Adaptation of Occupied Space
Occupied Space == Contention Region
Occupied space can be adapted by joint adaptations:
Rate-CS thresholdPower-CS threshold
Power-ratePower-rate-CS threshold
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Analytical Motivation for ProtocolsPre-MURI work [Yang05Infocom]
Cellular Model + Carrier Sense
SINR
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β = CSth / Rx th (dB)
Norm
aliz
ed A
ggre
gate
Th
roughput
Network Aggregate Throughput(curves for different network parameters)
For fixedpower,
optimal needsjoint
rate and CSthreshold
adaptation
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Dynamic Spatial Backoff
For fixedpower,
optimal needsjoint
rate and CSthreshold
adaptation
Joint adaptationof other parameters
can be justifiedsimilarly
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Dynamic Spatial BackoffJoint Rate and CS Threshold Adaptation
Adaptation as search
CS[1] CS[k]
Rate[2]
Rate[k]
CS[2] CS[k-1]
Rate
CS Threshold
Rate[k-1]
Rate[1]
2 dimensional space
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Towards a Protocol:Reduce Search Space
CS[1] CS[k]Rate[1]
Rate[k]
CS[2] CS[k-1]
Reduce search space using a lower bound on suitable CS threshold for a given rate
Rate
CS Threshold
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Towards a Protocol: Dynamic Search Using Transmission Success/Failure History
Rate
CS Threshold
CS[1] CS[2] CS[3] CS[4]rate[1]
rate[2]
rate[3]
rate[4]
V
V
V
> > >
Rate
CS Threshold
CS[1] CS[2] CS[3] CS[4]rate[1]
rate[2]
rate[3]
rate[4]
V
V
V
> > >
Success Failure
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Towards a Protocol: Other Components
Determining success or failure using current parameters
Using history to guide search
Successful combination of parameters cached
for future use
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Towards a Protocol
We have proposed a dynamic spatial backoff protocol that adapts rate and CS threshold
Similar mechanisms can be used for other joint adaptations
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Performance of Dynamic Spatial Backoff(Random Topology: 40 nodes)
β = CSth / Rx th (dB)
Agg
rega
te T
hrou
ghpu
t (M
bps)
101% of static optimal
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Performance of Dynamic Spatial Backoff(Random Topology: 16 nodes)
β = CSth / Rx th (dB)
Agg
rega
te T
hrou
ghpu
t (M
bps)
92% of static optimal
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Conclusion: Dynamic Spatial Backoff
Significant potential to optimize performanceusing distributed mechanisms
Challenges remain:
Accurately determining success versus failure
Fully distributed mechanisms can be sub-optimal
Interactions with higher layers
Integration with temporal contention resolution
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Thanks!
www.crhc.uiuc.edu/wireless
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Thanks!
www.crhc.uiuc.edu/wireless
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Random (c,f) Channel Assignment
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Channel Transition Strategy
(1, 2, 3)
(4, 5, 6)
Adjacent (6, 3) assignmentPreferred channels : 2,3,4,5
(3, 4, 5)
(4, 5, 6)
(2, 3, 4)
(2, 3, 4)
(1, 2, 3)
2
23
4
5
5(4, 5, 6)
(2, 3, 4)
( 3, 4, 5)
Use randomlychosen preferred
channel available atsource (channel 2)
Start transitions toget to a preferred channelat destination (channel 5)