Cooperative Comm v3-1
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Transcript of Cooperative Comm v3-1
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Indian Institute of Science (IISc), Bangalore, India
Cooperative Communications
Neelesh B. MehtaECE DepartmentIISc, Bangalore
Collaborators:
Andreas Molisch (MERL), Ritesh Madan (Flarion), Raymond Yim (Olin College),
Hongyuan Zhang (Marvell), Natasha Devroye (Harvard), Jin Zhang (MERL),
Jonathan Yedidia (MERL), Vinod Sharma (IISc), Gaurav Bansal (IISc)
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Indian Institute of Science, Bangalore
Motivation Behind Cooperative Communications
• Multiple antenna spatial diversity
using only single antenna nodes
• Exploit two fundamental aspects
of wireless channels:
– Broadcast
– Multiple access
s
r1
dr2
r3
r4
Cooperative relaysd
s2
Tw
o co operativ e sourc es
s1
h1d
h2d
h12
h1d
h4d
h2d
h3dhsd
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Indian Institute of Science, Bangalore
What’s Different Between MIMO and Cooperation?
• Distributed nature of relays/nodes
– Different channel gain amplitudes and phases
– Each relay runs on its own timer and VCO
• Relay capabilities
– Single antenna
– Full duplex or half duplex
• Channel state information (CSI)
– Relay might not know states of other relay links
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Indian Institute of Science, Bangalore
Outline
• Various cooperation schemes
• Cooperation in ad hoc networks
• Cooperation in infrastructure-based networks
• Cross-layer issues
• Other interesting topics
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Indian Institute of Science, Bangalore
Cooperative Communication Schemes
• Amplify and forward
• Decode and forward
• Estimate and forward
Possibilities:
• Orthogonal / Non-orthogonal cooperation
• Coded / Uncoded cooperation
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Indian Institute of Science, Bangalore
Analysis of Basic 3 Node Scenario
Performance metrics
• Outage
• Power consumption
• Diversity
• BER (Coded/Uncoded)
d
s2
Tw
o so urces
s1
h1d
h2d
h12
S1 transmits S2 transmits
d receives d receivesConventionalmodel
Tx
Rx
S1 tx S2 repeats S2 tx S1 repeats
d, S2 rx d rx d,S1 rx d rx Cooperative source model
Tx
Rx
[Laneman & Wornell, IEEE Trans. on Inf. Theory, 2004]
[Stefanov, Erkip, IEEE Trans. on Communications, 2004]
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Indian Institute of Science, Bangalore
Outage Analysis: Amplify and Forward
[1][1]
[2] [2]
sd dd
d rd sr rd r d
h wyx
y h h h w wβ β
= + +
2
0
r
sr s
P
h P Nβ ≤
+
2 22
2 2
SNR SNRlog 1 SNR
SNR SNR
sr rd sr rdAF sd sd
sr sr rd sr
h hI h
h h
÷= + + ÷+
d
r
s hsd
hrd
hsr
xyd
yr = hsr x + wr
( ) ( ) 222 2
2 2 2 2
2 11( , ) Pr
2 SNRsr
sd sr
Rrd
out AFrd
P SNR R I Rσ σ
σ σ σ−+
= < ≈
Relay power
constraint:
Tx. rate
Outage prob.
Diversity order = 2
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Indian Institute of Science, Bangalore
Outage Analysis: Decode and Forward
Case 1: Destination can decode only if relay decodes
ˆrx x= ˆd rd dy h x w= +
( ) ( )2 2 21min log 1 , log 12DF sr sd rdI SNR h SNR h SNR h = + + +
( )2
2
1 2 1( , ) Pr
R
out DFsr
P SNR R I RSNRσ
−= < ≈
(Assume codeword level decoding)
Diversity order = 1
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Indian Institute of Science, Bangalore
Outage Analysis: Adaptive Decode and Forward
Case 2: Source forwards to destination instead of relay if SR channel is
poor
ˆrx x= ˆd rd dy h x w= +
( )( )
22 2
2 2
1 2 1log 1 2 ,2
1log 1 , else2
R
sd sr
DF
sd rd
SNR h hSNRI
SNR h SNR h
−+ <= + +
( ) ( ) 222 2
2 2 2 2
2 11( , ) Pr
2
R
sr rdout DF
sd sr rd
P SNR R I RSNR
σ σσ σ σ
−+= < ≈
(Similar results apply for non-orthogonal scheme in which source transmits
to destination in both time slots, and relay repeats in second time slot)
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Indian Institute of Science, Bangalore
DF Coded Cooperation: An Explicit Example
• Codeword of N bits divided into two parts: N1 and N2
• In next frame:
– S2 relays N2 bits of S1 if it can decode it correctly
– Else, S2 sends its own N2 bits
[Hunter & Nosratinia, IEEE Trans. on Wireless Commn., 2006]
S1 bits S2 bits relay Inactive
Inactive S2 bits S1bits relay
S1
S2 Rx S1 bits
Rx S2 bits
N1 bits N2 bits N1 bits N2 bits
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Indian Institute of Science, Bangalore
Analysis: Pairwise Codeword Error Probability
• Slow fading
1 1 2 2
1 1 1( )
2 1 1d d
P dd SNR d SNR
= ÷ ÷+ +
( )1 1 2 2( ) 2 2d dP d Q d dγ γ= +
• Fast fading
1 2
1 2( ) 2 ( ) 2 ( )d dn n
P d Q n nη η
γ γ∈ ∈
= + ÷ ÷
∑ ∑1 2
1 1
1 1 1( )
2 1 1
d d
d d
P dSNR SNR
≤ ÷ ÷+ +
Diversity order = 2
Diversity order = Hamming distance
(Same for non-cooperation case)
SNR in first frame
SNR in second frame
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Indian Institute of Science, Bangalore
Other Cooperation Schemes
• Estimate and forward
– [Cover & El Gamal, IEEE Trans. Inf. Theory, 1979]
• Non-orthogonal transmission schemes
– Perform better at the expense of a more complicated destination
receiver [Nabar, Bolczkei, Kneubuhler, IEEE JSAC 2004]
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Indian Institute of Science, Bangalore
Cooperation in Ad Hoc Networks
• Basic 3 node scenario
• Multiple sources/relays case
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Indian Institute of Science, Bangalore
Extension to Multiple Node Scenarios
Non-orthogonal schemesOpen-loop scenario • Each relay that decodes
chooses its column of a pre-specified ST code matrix
(e.g., Orthogonal ST design)[Chakrabarti, Erkip, Sabharwal, Aazhang, IEEE Sig. Proc. Mag., 2007]
• Relay subset selection
Closed-loop scenario
• Relays that decode beamform together to destination
2 Repeats 11 Tx 3 Repeats 1 …... N Repeats 1
1 Repeats 22 Tx 3 Repeats 2 …... N Repeats 2
1 Repeats 33 Tx 2 Repeats 3 …... N Repeats 3
1 Repeats NN Tx 3 Repeats N …... N-1 repeats N
time
freq
uen c
y
Orthogonal scheme
[Laneman & Wornell, IEEE Trans. on Inf. Theory, 2003]
1 Tx D(1) subset repeats
2 Tx D(2) subset repeats
N Tx D(N) subset repeats
time
freq
uen c
y
Non-orthogonal scheme
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Indian Institute of Science, Bangalore
C
22 2
2
22
Cooperative Beamforming and its Feasibility
• Relays phase align and power control transmit signal
• Equivalent to a multi-antenna array at transmitter
• Two important practical issues
– CSI needs to be acquired
– Beamforming nodes need to be synchronized
1
1
1
1
1
1
C
Inter-cluster communications
[Ochiai, Mitran, Poor & Tarokh, IEEE Trans. Sig. Proc. 2005]
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Indian Institute of Science, Bangalore
Acquiring CSI in Cooperative Beamforming
s
r1
tr2
r3
r4
r5
xx
1. Broadcast data 2. Acquire CSI 3. Select relays
[Madan, Mehta, Molisch, Zhang, To appear in IEEE Trans. Wireless Commn., 2008]
• Acquiring CSI requires extra energy and time
s
r1
tr2
r3
r4
r5
Relay subset
selection by
destination
g1
g3
g2
h1
h2
h3
h5
s
r1
tr2
r3
r4
r5
4. Beamform data
α |g1|/(|g1|+|g3|)
α |g3|/(|g1|+|g3|)
x
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Indian Institute of Science, Bangalore
Trade-offs and Design Goals
• Broadcast power:
– Less power: Signal reaches fewer relays, lose out on diversity
– More power: Signal reaches more relays, but increases relay
training overhead
• Relay selection by destination:
– Select few relays: Lose out on diversity when transmitting data
– Select many/all relays: More feed back energy spent to reach less
and less useful relays
• Questions:
– Optimum relay subset selection rule (subject to outage constraint)?
– Energy savings achieved by cooperative beamforming?
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Indian Institute of Science, Bangalore
Average Energy Consumption: Including Cost of CSI
As a function of number of relays who decode message
Total energy consumed: Effect of relay selection rule
• Rule of thumb: Broadcast to reach 3-4 (best) relays, some of then beamform upon selection
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Indian Institute of Science, Bangalore
Synchronization for Cooperative Beamforming
• Performance robust to imperfect synchronization
• Example: Two equal amplitude signals from two
transmitters. Signals are offset by a phase w
– Resulting amplitude: |1+ ejω| = 2 cos(ω/2)
– Even if ω = 300, amplitude = 1.93 (instead of 2) – Off by only 4% !
[Mudumbai, Barriac & Madhow, IEEE Trans. Wireless Commn. 2007]
• General case:
[ ] [ ]
22
1
2
1.
12. 2 ( 1) cos
i
Nj
R ii
R i
P g e
E P N EN
ω
ω
==
= + −
∑
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Indian Institute of Science, Bangalore
Receive Power Distribution
Phase uniformly distributed between [-π/10, π/10]
[Mudumbai, Barriac & Madhow, IEEE Trans. Wireless Commn. 2007]
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Indian Institute of Science, Bangalore
Relay Selection: Relays Help Even When ‘Not Used’
• Full diversity achieved by just selecting single best relay
– Well understood classical result
• [Win & Winters, IEEE Trans. Commn. 1999]
• E.g., Antenna selection, Partial Rake CDMA receivers
– Simple to implement
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Indian Institute of Science, Bangalore
Relay Selection: Selection Criteria and Mechanisms
s
r1
dr2
r3
r4
h1
h2
h3
h4
g1
g2
g3
g4
Selection criteria:
• Depends on SR and RD channels
• Criteria: ( )2 2
2 2
2 2
1. min ,
2.
i i i
i ii
i i
h g
h g
h g
µ
µ
=
=+
[Blestsas, Khisthi, Reed & Lippman, IEEE JSAC, 2006; Luo et al, VTC 2005;
Lin, Erkip & Stefanov, IEEE Trans. on Commn., 2006]
• Multiple access relay selection mechanism:
– Relays overhear a RTS (request to send) from source, and
CTS (clear to send) from destination to estimate channels
– Each relay sets a timer with expiry 1/i it µµ
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Indian Institute of Science, Bangalore
Opportunistic Relay Selection and Cooperation Using Rateless Codes
• Rateless codes (e.g., digital fountain codes)– Convert a finite-length source word into an infinitely long
bitstream
– Receiver decodes successfully when received mutual information exceeds the entropy of the source word
– Receiver only needs to send a 1-bit ACK
• Ideal ‘binning’ properties of rateless codes1. Order in which bits received doesn’t matter
2. If destination receives data streams from N nodes, it accumulates mutual information from all N nodes
[Shokrollahi, ISIT 2004; Mitzenmacher, ITW 2004; Luby, FOCS 2002; Palanki & Yedidia, ISIT 2004;
Erez, Trott & Wornell, CoRR 2007]
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Indian Institute of Science, Bangalore
Asynchronous Cooperation With Rateless Codes
s
r1
dr2
r3
r4
s
r1
dr2
r3
r4
s
r1
dr2
r3
r4
Broadcast Best relay receives packet and starts transmitting to
destination
Second best relay also receives packet and starts transmitting to destination
[Molisch, Mehta, Yedidia, Zhang, IEEE Trans. Wireless Commn,
2007]
Time taken for best relay to decode packet: ( )( )2log 1 max i i
Bt
h=
+
h1
h4
h2
h3
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Indian Institute of Science, Bangalore
Performance: Transmission Energy & Time
Mean transmission time and energy usage
Energy usage statistics
Performance primarily depends on inter-relay link strength
Mea
n tx
.
ener
gy
Mean tx .
time
Number of
relays
CD
F (
tx.
time)
Tx. time (normalized)
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Indian Institute of Science, Bangalore
Cooperation in Infrastructure-Based Networks
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Indian Institute of Science, Bangalore
Cooperation in Infrastructure-Based Networks
• Downlink
– Base station cooperation
– Relay cooperation
• Uplink
– Similar to schemes we have seen thus far
• [Lee & Leung, IEEE Trans. Vehicular Technology, 2008]
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Indian Institute of Science, Bangalore
Base Station (BS) Cooperation
• Much more capable base stations (source nodes)
– Each base station possesses multiple transmit antennas
• CSI shared between base stations
– Extreme case: Full CSI at all BSs
• Benefit: Significantly better co-channel interference
management
BS1 BS2
MS1
MS2
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Indian Institute of Science, Bangalore
Giant MIMO Array: Transmission Techniques
• Linear precoding
– Generalized Zero Forcing (GZF)
– SLNR criterion based designs
– Sum rate criterion based designs
• Non-linear techniques
– Dirty paper coding
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Indian Institute of Science, Bangalore
Base Station Cooperation: Is It Giant MIMO?
No!
BS1 BS2
MS1
MS2
1H 2HSuper BS
MS1
MS2
12 , HH
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Indian Institute of Science, Bangalore
Interference is fundamentally asynchronous
• Even with perfect timing-advance!
(1)2H
(1)1H
(2)1H
(2)2H
(1)1τ
(2)2τ(1)
2τ
(2)1τ
BS1 BS2
MS2
MS10 0
(1) (1)2 1τ τ−
(2) (2)2 1τ τ−
[Zhang, Mehta, Molisch & Zhang, IEEE Trans. Wireless Commn. 2008]
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Indian Institute of Science, Bangalore
Implications on Fundamental System Model
( ) ( ) ( ) ( ) ( )
1 1 1
( ) ( ) ( )B K B
b b b b bk k k k k k jk k
b j b
m m m= = =
= + + ∑ ∑ ∑y H T s H T i n
( ) ( ) ( ) ( )
1 1 1
( ) ( ) ( ) ( )B K B
b b b bk k k k k j j k
b j b
m m m m= = =
= + + ∑ ∑ ∑y H T s H T s n
Changes the basic model!
Should be:
Was:
Generalized zero forcing constraint is no longer sufficient
Channel from BS b to MS k
Precoding at BS b for MS k
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Indian Institute of Science, Bangalore
Asynchronous Interference-Aware Precoding
• Linear precoding design methods
1. Sum rate maximization (CISVD)
– Non-trivial, non-convex
– Game theoretic approach in DSL: [Yu, Ginis, Cioffi ’02]
2. Mean square error minimization (JWF) – [Zhang, Wu, Zhou, Wang ‘05]
3. Signal to leakage plus noise ratio criterion (JLS) – [Tarighat, Sadek, Sayed ‘05][Dai, Mailaender, Poor ’04]
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Indian Institute of Science, Bangalore
Modeling Asynchronicity Helps
-5 0 5 10 15 200
2
4
6
8
10
12
Transmit SNR per User(dB)
Ave
rag
e S
pe
ctru
m E
ffici
en
cy P
er
Use
r(b
ps/
HZ
)
JWFJWF: Ignoring async. intf.JLSJLS: Ignoring async. intf.CISVDCISVD: Ignoring async. intf.
• Rate penalty for ignoring asynchronicity is significant
JWF
JLS
CISVD
Transmit SNR per user [dB]
Ave
. spe
ctra
l eff i
cien
cy
(bits
/s/H
z)
2 cell, 2 UE set up
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Indian Institute of Science, Bangalore
Relay Cooperation System Model
1 11 21 11 21 1 1
2 12 22 12 22 2 2
Y h h b b U N
Y h h b b U N
= +
Received signals
BS-MS channel
Linear precoding
Information symbols
AWGN
• Linear precoding at relays
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Indian Institute of Science, Bangalore
Asymmetric Relaying Arises Naturally
• Optimal asymmetric linear precoder is unknown!
• Can reduce the dimensionality of the optimization problem considerably
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Indian Institute of Science, Bangalore
Cross Layer Aspects of Cooperation
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Indian Institute of Science, Bangalore
Cross-Layer Aspects of Cooperation
• Cooperative MAC
– [Liu, Lin, Erkip, Panwar, IEEE Wireless Commn., 2006]
• Cooperative Hybrid ARQ
– [Zhao & Valenti, IEEE JSAC 2005]
• Cooperative routing
– General routing problem
– Progressive accumulative routing
• Queued cooperation
– [Mehta, Sharma, Bansal, Submitted, 2008]
• Impact of physical layer non-idealities
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Indian Institute of Science, Bangalore
Cooperative Multi-Hop Routing
• Which relay subset should cooperate in which step?
• Number of possibilities/step: 2N instead of N
• Channel fading: Drives how local the cooperation can be
s
r1
tr2
r3
r4
r5
r6
r7
r9
[Khandani, Abounadi, Modiano & Zheng, Allerton
2003]
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Indian Institute of Science, Bangalore
Reducing Problem to Conventional Routing Problem
• Only allow nodes k edges/hops apart to cooperate
• Construct hyper graph of neighbour nodes
• Determine optimal cooperation/non-cooperation scheme to transmit between
neighbours
• Assign energy cost to each edge in hyper graph
• Distributed conventional routing algorithms now applicable to determine best
multihop route from source to destination, e.g., Belman-Ford routing
[Madan, Mehta, Molisch, Zhang, Allerton 2007]
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Indian Institute of Science, Bangalore
Progressive (Energy) Accumulative Routing
s
r1
tr2
r3
r4r6
• Nodes do not discard previous transmissions in a route
• Energy-efficient unicast, multicast and broadcast
Unicast: [Yim, Mehta, Molisch & Zhang, IEEE Trans. Wireless Commn., 2008]Broadcast/Multicast routing: [Maric & Yates, IEEE JSAC 2002, 2005]
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Indian Institute of Science, Bangalore
1st Relay Addition: Necessary & Sufficient Conditions
• A node r helps if and only if
(Any eligible node can
overhear source to
destination transmission)
• Source (s) and relay (r) transmit powers for maximal power savings
s t
hrt > hst
(Relay doesn’t help)
hsr > hst
(Relay doesn’t help)
hst < min{hsr,hrt} (Relay saves power)
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Indian Institute of Science, Bangalore
Progressive Accumulative Routing: Protocol Designs
r
t
s
r
t
q
s
r
t
q
s t
u v
l
w
• Update routes without tearing them down
• Sufficient conditions to add a relay turn out to be nice!
• Packet header fields can be designed so that only local CSI is needed
• How to select optimal relays?
• Optimal relay transmission power?
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Indian Institute of Science, Bangalore
s t
u v
l
w
s t u v whwt hwv
MSrc
MDest
RSrc RDest RelayID
GainD GainR
Ready to cooperate packet
Data Packet and Cooperation Packet Structures
PAR Protocol q
s t u v hst/hsq + hqt/hqu hut huv
MSrc
MDest
RSrc RDest FracDelivered GainD GainR
Data
Local CSI info
u to v
w to u
1 1 1wt ut
uw uw uv
h h
h h h
>
+ <
Sufficient conditions to be a useful relay
Energy accumulated thus far
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Indian Institute of Science, Bangalore
Simulations: Gains from PAR
• 100 nodes distributed uniformly
in a grid of size 20 x 20 grid
• Source at (5,10) and destination
at (15,10)
• Total power consumption
decreases from 100% to 13.6%
to 2.84% to 1.47% and 1.35% in
5 iterations.
Box plot
Number of iterations
Tot
al p
o wer
con
s um
ed
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Indian Institute of Science, Bangalore
Other Aspects
• Network lifetime maximization and cooperation
– [Himsoon, Siriwongpairat, Han & Liu, IEEE JSAC 2007]
• Distributed detection and estimation using cooperation in
sensor networks
– [Nayagam, Shea & Wong, IEEE JSAC 2007]
• Cognitive radios and cooperation
– [Ganesan & Li, IEEE Trans. Wireless Commn 2007]
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Indian Institute of Science, Bangalore
Summary and Conclusions
• Cooperation effectively exploits three essential wireless characteristics:– Physical layer spatial diversity
– Broadcast advantage
– Multiple access characteristics of wireless
• Affects physical layer and higher layer design
• Some key problems: – General multihop scenarios
– Cross-layer design with cooperation
– Robust synchronization schemes
– Infrastructure-based cooperation in next generation wireless
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Indian Institute of Science, Bangalore
General Case: Multiple Relays (Between Two Relays)
• Sufficient condition for inclusion: Not conducive to distributed implementation
• Only two nodes adjust transmit powers
1 1and lt wt
uw uv wvuv uw lt ut
h hh h h
h h h h
−> − > ÷ −
1 1 1
uw wv uvh h h+ <• Weaker condition:
uuw
Ph
γ= 1 ( ) ut ut wtl
uw uv uw wv
h h hP A l
h h h h
γ = − + − − ÷
Energy accumulated
at last node (l)
s t
u v
l
w
Add node between two relays
(not after last relay)
(Parent
relay)
(Last
relay)
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Indian Institute of Science, Bangalore
Master-Slave Architecture for Phase Synchronization
[Mudumbai, Barriac & Madhow, IEEE Trans. Wireless Commn.
2007]