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Enhancing Cellular Multicast PerformanceUsing Ad Hoc Networks

Jun Cheol Park (jcpark@cs.utah.edu)Sneha Kumar Kasera (kasera@cs.utah.edu)

School of ComputingUniversity of Utah

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Why Multicast In Cellular Networks?

Transmitting data from single sender to multiple receivers

Why not use shared nature of wireless links?

Benefits Efficient resource

management Emergency communication

Base Station

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Receiver heterogeneity

Different, dynamic channel condition in wireless networks

Key impediment in multicast deployment

Base Station

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Impact of receiver heterogeneity HDR BCMCS (High Data Rate Broadcast and

Multicast Services) – 3G proposed standard Fixed data rate for each service More heterogeneity, much less average

throughput

0.010.020.030.040.050.060.070.080.090.0100.0

1 (1 2) (1 2 3) (1 2 3 4) (1 2 3 4 5)

Diversity of channel conditions

Average throughput / Served data rate (%)

(1)

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Outline

Combined Architecture BCMCS + Ad hoc

Ad hoc Paths Transmission Interference Model

Distance-2 Vertex Coloring MIND2 Routing Algorithm

Performance Benefits Summary

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Combined Architecture

proxy

Base Station

Multicast Members

Problematic node 802.11

802.11

802.11

BCMCS

Each node has dual interfaces:HDR + Wi-Fi

802.11

802.11

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Architecture UCAN (Unified Cellular and Ad-Hoc Network

Architecture): Haiyun Luo, et al. Mobicom’ 03 Unicast only Considers only HDR downlink condition of

proxies

Our approach In the context of multicast Considers achievable data rate of ad hoc path as well as HDR downlink condition

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How to find best ad hoc paths Achievable data rate of ad hoc path depends

upon transmission interference

Transmission interference can be modeled by interference graph Distance-2 vertex coloring

Transmission reduction factor in data rate of ad hoc path determined by minimum Distance-2 vertex

coloring

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Transmission Interference Model

Minimum number of colors for distance-2 vertex coloring matches with transmission reduction factor of ad hoc path

1 2 43 5

transmission range receiving range

4-hop ad hoc path

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Minimum Distance-2 Vertex Coloring

1

2

22

2 2

2 2

2

1

6

57

8 4

9 3

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Distance-1 Distance-2

Δ(G) = 8 where Δ(G) is maximum node degree

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Minimum Distance-2 Coloring Problem NP-complete Minimum solutions are mostly within upper

5% of Δ(G) + 1 (By A.H. Gebremedhin, 2004)

Minimum # of colors is approximated by Δ(G) + 1

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Effective data rate Achievable data rate of ad hoc path W/(Δ(G)+1)

W = achievable data rate of one-hop link HDR data rate of proxy p = Hp

Min{W/(Δ(G)+1), Hp}

MIND2 Rouging Algorithm Find a node that has maximal value of this effective

data rate

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Example

UCAN routing

MIND2 routing

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Simulation Setup in ns-2

Implement 3G HDR BCMCS

Implement MIND2 routing algorithm

Use IEEE 802.11b, 11Mbps

Uniform distribution of 100 nodes in a cell

# of Multicast members: N=20, 40, and 60

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Performance Gain

BCMCS+MI ND2 Over BCMCS

0%

50%

100%

150%

200%

250%

300%

614 Kbps 921 Kbps 1228 Kbps 1843 Kbps

Served data rate

Ave

rage

Goo

dpu

t Im

prov

emen

v (%

)

N=20 N=40 N=60

Goodput = Achievable throughput

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Performance Comparison Fluctuated better performance due to instability of

UCAN

BCMCS+MI ND2 Over BCMCS+UCAN

0%

10%

20%

30%

40%

50%

60%

614 Kbps 921 Kbps 1228 Kbps 1843 Kbps

Served data rate

Ave

rage

Goo

dpu

t Im

prov

emen

v (%

)

N=20 N=40 N=60

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Conclusion & Future work

Demonstrated receiver heterogeneity problem

Modeled transmission interference using distatance-2 vertex coloring

Developed an efficient routing algorithm, MIND2

Showed performance benefits of MIND2

Issues for future work Transmission interference model when links

are lossy Use of ad hoc multicast

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Thanks!

Any questions?

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Backup

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More Optimization Techniques Simple merge

If neighbor vk already has proxy p(vk), examine the value of Hp(vk)

One more lookaheadTvk = Min {rW/(Δ(Gvk)+1), H2p(vk)}

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Transmission interference on two ad hoc paths

Distance-2 Coloring:5 colors required

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Transmission Sequence