Network Coding in Cooperative Communications: Friend or

Foe?

Sushant Sharma, Yi Shi, Jia Liu, Y. Thomas Hou, Sastry Kompella, and Scott F. Midkiff

This paper was completed while authors inVirginia Polytechnic Institute and State University

(Virginia Tech).

Published in IEEE Transactions on Mobile Computing, June, 2011.Manuscript received 27 May 2010; revised 19 Jan. 2011 and 16 May 2011; accepted 20 May 2011.

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Previous works of this paper (1)

• Cooperative communication in relay networks– Yi Shi, Sushant Sharma, Y. Thomas Hou and Sastry Kompella, “Optimal

Relay Assignment for Cooperative Communications,” in ACM MobiHoc 2008.

Sushant Sharma, Yi Shi, Y. Thomas Hou and Sastry Kompella, “An Optimal Algorithm for Relay Node Assignment in Cooperative Ad Hoc Networks,” in IEEE/ACM Transactions on Networking, volume 19, issue 3, pages 879-892, Junse 2011.

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Cooperative communication in relay networks (1)

• Relay network– Add relay nodes to increases transmission rate

and network coverage.

dsc1 (Low)

rc2 (High) c3 (High)

2 3 1

1 1 1c c c

Relay transmission is faster.

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Cooperative communication in relay networks (2)

• Cooperative communication– Combine direct and relay signals.• 1st slot: s sends signal to r and d.• 2nd slot: r relays signal to d.• Receiver d combine the two signals.

– Better transmission rate

dsc1 (Low)

rc2 (High) c3 (High)

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Previous works of this paper (2)• Routing and Network Coding with CC

– Sushant Sharma, Yi Shi, Y. Thomas Hou, Hanif D. Sherali, and Sastry Kompella, ”Cooperative Communications in Multi-hop Wireless Networks: Joint Flow Routing and Relay node Assignment,” in IEEE INFOCOM 2010

Sushant Sharma, Yi Shi, Y. Thomas Hou, Hanif D. Sherali, Sastry Kompella, and Scott F. Midkiff, “Joint Flow Routing and Relay Node Assignment in Cooperative Multi-hop Networks,” in IEEE Journal on Selected Areas in Communications (JSAC), Special Issue on Cooperative Networking – Challenges and Applications, to appear 2012.

– Sushant Sharma, Yi Shi, Jia Liu, Y. Thomas Hou and Sastry Kompella, “Is Network Coding Always Good for Cooperative Communications?”in IEEE INFOCOM 2010.

This paper

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XOR Network coding– Inter-session network coding: the information/packets of

different sessions/flows can be coded together.• Can improve the network throughput further .

S2

Y

S1

X

Traditional computer networks Network Coding

a

b2

a b

b

Bottleneck link (X,Y) requires two time units to transmit packets a and b.

(X,Y) transmits ab in a time unit.R1 decodes b from a and ab.R2 decodes a from b and ab.

R1 R2

Send packet a to R1,R2

Send packet b to R1,R2

S2

Y

S1

X

a

b2

a b

b

R1 R2

Send packet a to R1,R2

Send packet b to R1,R2

a

a

b

b

ab

ab ab

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Network Coding in Cooperative Communication (NC-CC)

• Transmission Scheme

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Analysis of achievable rate under two scenarios

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The Case of ANC-CC (1)

• Analog NC-CC Transmission behavior

0~T

T~2T

2T~3T

x1

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Computing Achievable Rate

• Direct Transmission

• AN CC (without ANC) Shannon Capacity

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Computing Achievable Rate (cont’d)

• ANC-CC

Additional noise from relay

Additional noise from combination.

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The Case of DNC-CC

• Digital NC-CC1. Source node s0 transmits a signal in the

first time slot. This signal is received and decoded by the relay node; and overheard by the other destination node d1.

2. Source node s1 transmits and the signal is received and decoded by r; and overheard by d0.

3. The relay node combines the two decoded signals using DNC, and then transmits the combined signal.

0~T

T~2T

2T~3T

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Computing Achievable Rate(DNC-CC)

• It is well known that in general, when multiple source nodes transmit data at different rates, the optimal DNC strategy remains unknown.

• In addition to combining bits at the relay node, extraction and signal combination also need to be carried out at destination nodes.

• An upper bound for the transmission rate can be obtained by having every source node transmit at the maximum possible rate at which relay node can decode the data.

Upper bound:

Lower bound:

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Computing Achievable Rate(DNC-CC)

• Analyzing reception rate at a destination node

– The rate at which destination node di can receive signal xi is

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Computing Achievable Rate(DNC-CC)

• DNC-CC Achievable Rate

• DF CC

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Numerical Results (1)• Two-Session Networks– NC as a Friend• ANC

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Numerical Results (2)• Two-Session Networks– NC as a Friend• DNC

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Numerical Results (3)• Two-Session Networks– NC as a Foe• ANC

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Numerical Results (4)• Two-Session Networks– NC as a Foe• DNC

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Numerical Results (5)

• A General Multi-Session Network

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ANC:

DNC:

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Conclusion• In this paper, we investigated the fundamental problem of how NC

can affect the performance of CC.• NC can be both a friend or a foe of CC, depending on the

underlying network setting.

• This paper just discuss the simple problem where there are multiple sessions and only one relay node.

• A MAC problem could be how to group sessions and assign time slot for each session.– Sushant Sharma, Yi Shi, Y. Thomas Hou, Hanif D. Sherali, and Sastry Kompella, Optimizing

Network-Coded Cooperative Communications via Joint Session Grouping and Relay Node Selection,” in IEEE INFOCOM 2011

– Sushant Sharma, Yi Shi, Y. Thomas Hou, Sastry Kompella, and Scott F. Midkiff, “Optimal Grouping and Matching for Network-Coded Cooperative ommunications,” in IEEE MILCOM 2011.

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Comments

• How to effectively take advantage of Network-Coding is still a challenge.– When? (Transmission Scheduling)– How? (Coding Scheme)– Where? (Routing)– Control end-to-end delay and throughput with NC

is more difficult • Multi-hop• Interference control