Improving Performance of Wireless Networks

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Improving Performance ofWireless Networks

Nitin Vaidya

Joint work with Fan Wu, Tae Hyun Kim, Jian Ni,Vijay Raman, R. Srikant

November 4, 2010

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What Makes Wireless Networks Interesting?

Many forms of diversity

•Time

•Route

•Antenna

•Spatial

•Channel

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Multi-Channel Environments

Available spectrum

2 3 4 … c

Spectrum divided into channels

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Multi-Channel Wireless Networks

Benefits of channelization

g Channel diversity •Gain variations

•Interference mitigation

g Channel access efficiency gain

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Recent Contributions onMulti-Channel Networks

g Incorporating opportunism in multi-channel networks

g Improving channel utilization

g Game theoretic approach for channel management

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Opportunistic Routing

Opportunism

g Traditional routing: S R D

g But D may sometimes overheard S R transmission

g No need to forward such packets on R D

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S R D

Opportunism using MOREg Source sends linear combinations of packets in batches

g Forwarders keep all heard packets in a buffer

g Nodes transmit linear combinations of buffered packets

g Destination decodes once it receives enough combinations

S R D

P1

P2

P3

P1 P2 P3 =+ b + ca a,b,c

2,1,3

0,2,1

2,1,3

P1 P2 P3 =+ 1 + 32 2,1,3P1 P2 P3 =+ 2 + 10 0,2,1P1 P2 P3 =+ 0 + 23 3,0,2

3,0,2

=2 + 1 0,2,1 7,4,92,1,3 + 1 3,0,2

7,4,9

=2 + 2 0,2,1 1,6,62,1,3 - 1 3,0,2

1,6,6

P1

P2

P3

Opportunism versus Concurrency

g For opportunistic scheme to work,nodes must be on the same channel

g Reduces concurrency

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S R D

Trade-Off

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Advantages Disadvantages

Opportunism

Exploits broadcast nature

Reduces average # hops

Fewer transmissions

Higher contention

No multiple channel support

Multichannel

Concurrency

Lower contention

No opportunistic overhearing

Potentially longer routes

Example

g Traditional Channel Assignment

S

A

D

0.25

0.5

B

0.250.75

C1

C1

C2

C2

C3

0.75

C3

C3

0.9

End-to-end throughput = 0.5

Loss probability

“Opportunism-Aware” Channel Assignment

S

A

D

0.25

0.5

B

0.2

5

0.75

C1

C1

C1

C2

C2

0.75

C2

0.9

C1 C2

End-to-end throughput = 0.6475

Our Contribution

g Take into account both opportunistic gains obtained by assigning identical channels to the nodes, as well as concurrency gains by assigning different channels

g Extended MORE to a multi-radio multi-channel (MRMC) environment

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Summary

g Opportunistic schemes can benefit in multi-channel environments

g Channel assignment needs to be opportunism-aware

g Proposed such an assignment scheme

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Packet Size-Dependent Channel Selection

Channel Width

g Typically, channels are assumed identical width

g May benefit by varying channel widths

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2 3 4 … c1

Motivation

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Rate-independent MAC overhead

L1 bitsDIFS

)/(Overhead

RLT

T

i

Header

L1 /R

L2 bitsDIFS Header

T

L2 /R

MAC Overhead vs Packet Size

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Packet size Li

T = 50μs; R = 54 Mbps )/(

OverheadRLT

T

i

Current Approach

g Frame Aggregation (used in IEEE 802.11n)

Aggregate and send multiple packets in a single transmission opportunity

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L1 bitsDIFS Header L2 bits L3 bits

overhead Multiple packets to amortize overhead

Packet Size-Dependent Channel Widths

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g Partition a channel into narrow and wide sub-channels

g Use narrow sub-channel for short packets

g Use wide sub-channel for long packets

Proof-of-Concept

g Consider a node (A) communicating withmultiple other nodes

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A

Proposed Approach

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1Clients estimate ownshort packet load,and inform node A

Node A estimates aggregate short packet load2

Node A determines partition {BWS, BWL}3

Clients use BWS for short

& BWL for long packets4

Summary

g Channel width selection based on packet size distribution

g Can perform better than frame aggregation

g Ideas can be extended to arbitrary networks

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CSMA with Imperfect Carrier Sensing

Carrier Sensing (CS)

g Not perfect

g With narrower channels and mobility,fading can be an issue

g What happens to network performance whenCS is imperfect ?

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Throughput-Optimal Schedulers

g A scheduler is throughput-optimal ifit can serve all schedulable traffic

g Throughput-optimal scheduler byTassiulas-Ephremides’92

•Schedule =

•Computationally complex and centralized solution

Related Work

g Continuous-time CSMA-like algorithm by Jiang-Walrand’08

g Discrete-time CSMA by Ni-Srikant’09

Our Contribution:Preemptive CSMA

g Discrete-time medium accessg Per-packet scheduling decisiong Data packet collisions modeledg Non-zero carrier sense time

Analysis for

g Perfect carrier sensingg Imperfect carrier sensing

Model

g Link-centric model

i Transmission rate is normalized to 1

i One-hop traffic

g Conflict graph to model interference

Medium Access Model

Last α-duration of each time slot for carrier sense

Preemptive CSMA

g Two access probabilities: ai and pi

Carrier sense

u(t): preemptionx(t): transmission scheduleCi: set of conflict links of i

ACK reception

Performance Analysis

g Schedule evolution: discrete-time reversible Markov chain

Stationary distribution

iCu : set of conflicting links of links in u

iWhen pi = 1 - =

exp{wi(qi)} -1exp{wi(qi)}

1exp{wi(qi)}

Throughput-Optimality

g Preemptive CSMA is throughput-optimal

i When access probabilities are

• 0 < aLB ≤ ai ≤ aUB < 1

• pi = 1 - 1/exp{wi(qi)} where wi is a strict concave function with wi(0) = 0

i Proof relies on time-scale separation

•At each time slot, the Markov chain in the steady state

Carrier Sense Failure

g i.i.d. failure events over time slots and links

g Two types of carrier sense failures

•False positive– No activity, but busy state sensed– False positive with probability η

•False negative:– Activity, but idle state sensed– False negative with probability γ

Carrier Sense Failure:Main Result

g By choosing small enough access probability, possible to stabilize arbitrarily large fraction of capacity region

Proof complexity:Markov chain is no longer reversibleUse perturbation theory for Markov chains

Summary

Preemptive CSMA

gGood performance achievable despite imperfect carrier sensing

gSmall access probability overcomes the effect of carrier sensing failures

Where are we now ?

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What Makes Wireless Networks Interesting?

Many forms of diversity

•Time

•Route

•Antenna

•Spatial

•Channel

Wireless Diversity

g This project has furthered our understanding of approaches to wireless diversity using suitable protocols

g We now have a better understanding ofcross-layer protocol design

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What Remains?

g Physical layer community has also been making significant progress

– Interference alignment– Cooperation– Security

g Need to incorporate these ideas intothe protocol stack

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Natural continuationof DAWN MURI

What Remains?

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HigherLayers

UnicastMulticast

PhysicalLayer

DistributedApplications

What Remains?

Much attention to

g Moving bits betweennodes in the network

•throughput

•delay, jitter

•packet loss

g Cross layer ~ Layers 1-2-3

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HigherLayers

UnicastMulticast

PhysicalLayer


What Remains?

g Not as much attention to semantics ofdistributed applications

g How to exploitapplication-awareness ?

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HigherLayers

UnicastMulticast

PhysicalLayer


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HigherLayers

UnicastMulticast

PhysicalLayer


DistributedPrimitives

Wireless Network-AwareDistributed Primitives

Example primitives:gOrdered group communicationgConsensusgAggregationgSynchronizationgCoordination

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HigherLayers

UnicastMulticast

PhysicalLayer




Example primitives:gOrdered group communicationgConsensusgAggregationgSynchronizationgCoordination

Network-awarenessgWireless capacity regiongDiversitygBroadcast capabilitygEnergy constraints

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HigherLayers

UnicastMulticast

PhysicalLayer




Past Work on Middleware

g Similar motivation

g But optimized for wired networkswith high capacityand more benign characteristics

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g Wired algorithms not efficientg Do not exploit wireless capabilities

Many (new) fundamental problems open

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Distributed Algorithms & Networking

g Overlapping scope

g But cultures differ

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Communications / Networking

Distributed Algorithms

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DistributedAlgorithms

Black box networks

Emphasis onorder complexity

Emphasis on “exact”performance metrics

Constants matter


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Black box networks



Constants matter

Information transfer(typically “raw” info)


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Computationaffects communication


Constants matter

Information transfer(typically “raw” info)


Black box networks


Picture from Wikipedia

Beneficial to bring together researchers inwireless networking & distributed algorithms


Nitin H Vaidya

Creation of Adam - fresco in Sistine Chapel, by Michelangelo

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Thanks!

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Thanks!

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Thanks!

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Thanks!

Scheduling Example

A

B

C

PROBE

ACK

DATA

PROBE

ACK

DATA

PROBE

ACK

DATA DATAPROBE

PROBE

Access by aA

Access by aB

Access by aB

Access by pB

Sensed busy by Link A &

C

Preempted by Link

B

Sensed idle by

Link A & C

Preempted by Link A

& CConflict

graph forlinks A, B, C

Improving Performance of Wireless Networks

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