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Improving Performance of Wireless Networks. Nitin Vaidya Joint work with Fan Wu, Tae Hyun Kim, Jian Ni, Vijay Raman, R. Srikant November 4, 2010. What Makes Wireless Networks Interesting?. Many forms of diversity Time Route Antenna Spatial Channel. Multi-Channel Environments. - PowerPoint PPT Presentation
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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
DistributedApplications
What Remains?
g Not as much attention to semantics ofdistributed applications
g How to exploitapplication-awareness ?
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HigherLayers
UnicastMulticast
PhysicalLayer
DistributedApplications
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HigherLayers
UnicastMulticast
PhysicalLayer
DistributedApplications
DistributedPrimitives
Wireless Network-AwareDistributed Primitives
Example primitives:gOrdered group communicationgConsensusgAggregationgSynchronizationgCoordination
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HigherLayers
UnicastMulticast
PhysicalLayer
DistributedApplications
DistributedPrimitives
Wireless Network-AwareDistributed Primitives
Example primitives:gOrdered group communicationgConsensusgAggregationgSynchronizationgCoordination
Network-awarenessgWireless capacity regiongDiversitygBroadcast capabilitygEnergy constraints
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HigherLayers
UnicastMulticast
PhysicalLayer
DistributedApplications
DistributedPrimitives
Wireless Network-AwareDistributed Primitives
Past Work on Middleware
g Similar motivation
g But optimized for wired networkswith high capacityand more benign characteristics
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Wireless Network-AwareDistributed Primitives
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
Communications / Networking
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DistributedAlgorithms
Black box networks
Emphasis onorder complexity
Emphasis on “exact”performance metrics
Constants matter
Information transfer(typically “raw” info)
Communications / Networking
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DistributedAlgorithms
Computationaffects communication
Emphasis on “exact”performance metrics
Constants matter
Information transfer(typically “raw” info)
Communications / Networking
Black box networks
Emphasis onorder complexity
Picture from Wikipedia
Beneficial to bring together researchers inwireless networking & distributed algorithms
Wireless Network-AwareDistributed Primitives
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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