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 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

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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

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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


Use c channels


f

Even when f=2, get capacity benefit of √c

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Use c channels


f

Even when f=2, get capacity benefit of √c

cf

cf

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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)


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)


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!


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Thanks!


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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

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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)

Using Multiple Channels and Spatial Backoff to Improve Wireless Network Performance

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